The early stages of video editing present many cognitively demanding tasks that require editors to remember and structure large amounts of video. In our formative work we learned that editors break down the editing process into smaller parts by labeling and organizing footage around central themes. Using current video editing tools, this process is slow and largely manual. We present a system called ChunkyEdit for helping editors group video interview clips into thematically coherent chunks, which can then be exported to existing video editing tools and composed into an edited narrative. By focusing on this intermediate step, we leverage computation to do tedious organizational tasks, while preserving the editor’s ability to control the primary storytelling decisions. We explore four different topic modeling approaches to creating video chunks. We then evaluate our tool with eight professional video editors to learn how a chunking-based approach could be incorporated into video editing workflows.

Mix-and-match character creation tools enable users to quickly produce 2D character illustrations by combining various predefined accessories, like clothes and hairstyles, which are represented as separate, interchangeable artwork layers. However, these accessory layers are often designed to fit only the default body artwork, so users cannot modify the body without manually updating all the accessory layers as well. To address this issue, we present a method that captures and preserves important relationships between artwork layers so that the predefined accessories adapt with the character’s body. We encode these relationships with four types of constraints that handle common interactions between layers: (1) occlusion, (2) attachment at a point, (3) coincident boundaries, and (4) overlapping regions. A rig is a set of constraints that allow a motion or deformation specified on the body to transfer to the accessory layers. We present an automated algorithm for generating such a rig for each accessory layer, but also allow users to select which constraints to apply to specific accessories. We demonstrate how our system supports a variety of modifications to body shape and pose using artwork from mix-and-match data sets.

Responsive visualizations adapt to effectively present information based on the device context. Such adaptations are essential for news content that is increasingly consumed on mobile devices. However, existing tools provide little support for responsive visualization design. We analyze a corpus of 231 responsive news visualizations and discuss formative interviews with five journalists about responsive visualization design. These interviews motivate four central design guidelines: enable simultaneous cross-device edits, facilitate device-specific customization, show cross-device previews, and support propagation of edits. Based on these guidelines, we present a prototype system that allows users to preview and edit multiple visualization versions simultaneously. We demonstrate the utility of the system features by recreating four real-world responsive visualizations from our corpus.

An artist faces two challenges when creating a 2D animated character to mimic a specific human performance. First, the artist must design and draw a collection of artwork depicting portions of the character in a suitable set of poses, for example arm and hand poses that can be selected and combined to express the range of gestures typical for that person. Next, to depict a specific performance, the artist must select and position the appropriate set of artwork at each moment of the animation. This paper presents a system that addresses these challenges by leveraging video of the target human performer. Our system tracks arm and hand poses in an example video of the target. The UI displays clusters of these poses to help artists select representative poses that capture the actor's style and personality. From this mapping of pose data to character artwork, our system can generate an animation from a new performance video. It relies on a dynamic programming algorithm to optimize for smooth animations that match the poses found in the video. Artists used our system to create four 2D characters and were pleased with the final automatically animated results. We also describe additional applications addressing audio-driven or text-based animations.

We present RealitySketch, an augmented reality interface for sketching interactive graphics and visualizations. In recent years, an increasing number of AR sketching tools enable users to draw and embed sketches in the real world. However, with the current tools, sketched contents are inherently static, floating in mid-air without responding to the real world. This paper introduces a new way to embed dynamic and responsive graphics in the real world. In RealitySketch, the user draws graphical elements on a mobile AR screen and binds them with physical objects in real-time and improvisational ways, so that the sketched elements dynamically move with the corresponding physical motion. The user can also quickly visualize and analyze real-world phenomena through responsive graph plots or interactive visualizations. This paper contributes to a set of interaction techniques that enable capturing, parameterizing, and visualizing real-world motion without pre-defined programs and configurations. Finally, we demonstrate our tool with several application scenarios, including physics education, sports training, and in-situ tangible interfaces.

Abstract Creating expressive animated data graphics often requires designers to possess highly specialized programming skills. Alternatively, the use of direct manipulation tools is popular among animation designers, but these tools have limited support for generating graphics driven by data. Our goal is to inform the design of next‐generation animated data graphic authoring tools. To understand the composition of animated data graphics, we survey real‐world examples and contribute a description of the design space. We characterize animated transitions based on object, graphic, data, and timing dimensions. We synthesize the primitives from the object, graphic, and data dimensions as a set of 10 transition types, and describe how timing primitives compose broader pacing techniques. We then conduct an ideation study that uncovers how people approach animation creation with three authoring paradigms: keyframe animation, procedural animation, and presets & templates. Our analysis shows that designers have an overall preference for keyframe animation. However, we find evidence that an authoring tool should combine these three paradigms as designers’ preferences depend on the characteristics of the animated transition design and the authoring task. Based on these findings, we contribute guidelines and design considerations for developing future animated data graphic authoring tools.

Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and aesthetic appeal. When a pattern is encoded as a flat image without the underlying structure, manually editing the pattern is tedious and challenging as one has to both preserve the individual element shapes and their original relative arrangements. State-of-the-art deep learning frameworks that operate at the pixel level are unsuitable for manipulating such patterns. Specifically, these methods can easily disturb the shapes of the individual elements or their arrangement, and thus fail to preserve the latent structures of the input patterns. We present a novel differentiable compositing operator using pattern elements and use it to discover structures, in the form of a layered representation of graphical objects, directly from raw pattern images. This operator allows us to adapt current deep learning based image methods to effectively handle patterns. We evaluate our method on a range of patterns and demonstrate superiority in the context of pattern manipulations when compared against state-of-the-art pixel- or point-based alternatives.

We present a system that augments live presentation videos with interactive graphics to create a powerful and expressive storytelling environment. Using our system, the presenter interacts with the graphical elements in real-time with gestures and postures, thus leveraging our innate, everyday skills to enhance our communication capabilities with the audience. However, crafting such an interactive and expressive performance typically requires programming, or highly-specialized tools tailored for experts. Our core contribution is a flexible, direct manipulation UI which enables amateurs and experts to craft such presentations beforehand by mapping a variety of body movements to a wide range of graphical manipulations. By simplifying the mapping between gestures, postures, and their corresponding output effects, our UI enables users to craft customized, rich interactions with the graphical elements. Our user study demonstrates the potential usage and unique affordance of this mixed-reality medium for storytelling and presentation across a range of application domains.

We explore the use of hand gestures for authoring animations in virtual reality (VR). We first perform a gesture elicitation study to understand user preferences for a spatiotemporal, bare-handed interaction system in VR. Specifically, we focus on creating and editing dynamic, physical phenomena (e.g., particle systems, deformations, coupling), where the mapping from gestures to animation is ambiguous and indirect. We present commonly observed mid-air gestures from the study that cover a wide range of interaction techniques, from direct manipulation to abstract demonstrations. To this end, we extend existing gesture taxonomies to the rich spatiotemporal interaction space of the target domain and distill our findings into a set of guidelines that inform the design of natural user interfaces for VR animation. Finally, based on our guidelines, we develop a proof-of-concept gesture-based VR animation system, MagicalHands. Our results, as well as feedback from user evaluation, suggest that the expressive qualities of hand gestures help users animate more effectively in VR.

As a rule, user interface designers work iteratively. Over the course of a project, they repeatedly gather feedback, typically through in-person meetings, and update their designs accordingly. Through formative work, we find that design software tools do not support designers in managing meeting notes and previous design iterations as a cohesive whole. This causes designers to rely on ad-hoc practices for organizing work, which makes it hard for them to keep track of relevant feedback and explain their design decisions. To address this problem, we present Charrette, a system that allows designers to curate design iterations, attach meeting notes to the relevant content, and navigate sequences of design iterations with the associated notes to facilitate in-person discussions. In an exploratory user study, we evaluate how Charrette affects designers' self-reported ease in handling feedback during face-to-face discussions, compared with using their own tools. We find that using Charrette correlates with increased confidence and recall in discussing previous design decisions.

Interface designers often use screenshot images of example designs as building blocks for new designs. Since images are unstructured and hard to edit, designers typically reconstruct screenshots with vector graphics tools in order to reuse or edit parts of the design. Unfortunately, this reconstruction process is tedious and slow. We present Rewire, an interactive system that helps designers leverage example screenshots. Rewire automatically infers a vector representation of screenshots where each UI component is a separate object with editable shape and style properties. Based on this representation, the system provides three design assistance modes that help designers reuse or redraw components of the example design. The results from our quantitative and user evaluations demonstrate that Rewire can generate accurate vector representations of interface screenshots found in the wild and that design assistance enables users to reconstruct and edit example designs more efficiently compared to a baseline design tool.

Video summaries are a popular way to share important events, but creating good summaries is hard. It requires expertise in both capturing and editing footage. While hiring a professional videographer is possible, this is too costly for most casual events. An alternative is to place 360 video cameras around an event space to capture footage passively and then extract regular field-of-view (RFOV) shots for the summary. This paper focuses on the problem of extracting such RFOV shots. Since we cannot actively control the cameras or the scene, it is hard to create "ideal' shots that adhere strictly to traditional cinematography rules. To better understand the tradeoffs, we study human preferences for static and moving camera RFOV shots generated from 360 footage. From the findings, we derive design guidelines. As a secondary contribution, we use these guidelines to develop automatic algorithms that we demonstrate in a prototype user interface for extracting RFOV shots from 360 videos.

Performance animation is an expressive method for animating characters through human performance. However, character motion is only one part of creating animated stories. The typical workflow also involves writing a script, coordinating actors, and editing recorded performances. In most cases, these steps are done in isolation with separate tools, which introduces friction and hinders iteration. We propose TakeToons, a script-driven approach that allows authors to annotate standard scripts with relevant animation events like character actions, camera positions, and scene backgrounds. We compile this script into a story model that persists throughout the production process and provides a consistent structure for organizing and assembling recorded performances and propagating script or timing edits to existing recordings. TakeToons enables writing, performing and editing to happen in an integrated and interleaved manner that streamlines production and facilitates iteration. Informal feedback from professional animators suggests that our approach can benefit many existing workflows supporting individual authors and production teams with many different contributors.

We present a new example-based approach for synthesizing hand-colored cartoon animations. Our method produces results that preserve the specific visual appearance and stylized motion of manually authored animations without requiring artists to draw every frame from scratch. In our framework, the artist first stylizes a limited set of known source skeletal animations from which we extract a style-aware puppet that encodes the appearance and motion characteristics of the artwork. Given a new target skeletal motion, our method automatically transfers the style from the source examples to create a hand-colored target animation. Compared to previous work, our technique is the first to preserve both the detailed visual appearance and stylized motion of the original hand-drawn content. Our approach has numerous practical applications including traditional animation production and content creation for games.

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DepthCut , a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 18 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings. (Code and datasets will be made available.)

Vectorization turns photographs into vector art. Manual vectorization, where the artist traces over the image by hand, requires skill and time. On the other hand, automatic approaches allow users to generate a result by setting a few global parameters. However, global settings often leave too much detail/complexity in some parts of the image while missing important details in others. We propose interactive vectorization tools that offer more local control than automatic systems, but are more powerful and high-level than simple curve editing. Our system enables novices to vectorize images significantly faster than even experts with state-of-the-art tools.

When bringing animated characters to life, artists often augment the primary motion of a figure by adding secondary animation -- subtle movement of parts like hair, foliage or cloth that complements and emphasizes the primary motion. Traditionally, artists add secondary motion to animated illustrations only through arduous manual effort, and often eschew it entirely. Emerging ``live' performance applications allow both novices and experts to perform the primary motion of a character, but only a virtuoso performer could manage the degrees of freedom needed to specify both primary and secondary motion together. This paper introduces physically-inspired rigs that propagate the primary motion of layered, illustrated characters to produce plausible secondary motion. These composable elements are rigged and controlled via a small number of parameters to produce an expressive range of effects. Our approach supports a variety of the most common secondary effects, which we demonstrate with an assortment of characters of varying complexity.

Live animation of 2D characters is a new form of storytelling that has started to appear on streaming platforms and broadcast TV. Unlike traditional animation, human performers control characters in real time so that they can respond and improvise to live events. Current live animation systems provide a range of animation controls, such as camera input to drive head movements, audio for lip sync, and keyboard shortcuts to trigger discrete pose changes via artwork swaps. However, managing all of these controls during a live performance is challenging. In this work, we present a new interactive system that specifically addresses the problem of triggering artwork swaps in live settings. Our key contributions are the design of a multi-touch triggering interface that overlays visual triggers around a live preview of the character, and a predictive triggering model that leverages practice performances to suggest pose transitions during live performances. We evaluate our system with quantitative experiments, a user study with novice participants, and interviews with professional animators.

A common way to generate high-quality product images is to start with a physically-based render of a 3D scene, apply image-based edits on individual render channels, and then composite the edited channels together (in some cases, on top of a background photograph). This workflow requires users to manually select the right render channels, prescribe channel-specific masks, and set appropriate edit parameters. Unfortunately, such edits cannot be easily reused for global variations of the original scene, such as a rigid-body transformation of the 3D objects or a modified viewpoint, which discourages iterative refinement of both global scene changes and image-based edits. We propose a method to automatically transfer such user edits across variations of object geometry, illumination, and viewpoint. This transfer problem is challenging since many edits may be visually plausible but non-physical, with a successful transfer dependent on an unknown set of scene attributes that may include both photometric and non-photometric features. To address this challenge, we present a transfer algorithm that extends the image analogies formulation to include an augmented set of photometric and non-photometric guidance channels and, more importantly, adaptively estimate weights for the various candidate channels in a way that matches the characteristics of each individual edit. We demonstrate our algorithm on a variety of complex edit-transfer scenarios for creating high-quality product images.

We present an interactive design system to create functional mechanical objects. Our computational approach allows novice users to retarget an existing mechanical template to a user-specified input shape. Our proposed representation for a mechanical template encodes a parameterized mechanism, mechanical constraints that ensure a physically valid configuration, spatial relationships of mechanical parts to the user-provided shape, and functional constraints that specify an intended functionality. We provide an intuitive interface and optimization-in-the-loop approach for finding a valid configuration of the mechanism and the shape to ensure that higher-level functional goals are met. Our algorithm interactively optimizes the mechanism while the user manipulates the placement of mechanical components and the shape. Our system allows users to efficiently explore various design choices and to synthesize customized mechanical objects that can be fabricated with rapid prototyping technologies. We demonstrate the efficacy of our approach by retargeting various mechanical templates to different shapes and fabricating the resulting functional mechanical objects.

In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand, freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper, we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment. Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw, place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length, area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data. When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven graphics.

Advances in digital fabrication (DF) technologies are making it easier to produce high-fidelity replicas of digital designs. However, this push-to-print paradigm limits the creative opportunities that arise from the process of "working through a material" which involves risk, uncertainty, and serendipitous discovery. We investigate how DF artifacts can function as static intermediary tools, which we term proxies, to support crafting practice. We focus on the wire-wrapping process where physical wire is bent into complex shapes and build DF fixtures to aid with construction and fabrication. We explore how these proxies can be generated to provide users with different levels-of-assistance and evaluate how these proxies affect the making process. We show that our proxies affect quality and speed and yield different making experiences between novice and expert craftspeople. We derive design principles to inform future proxy design and discuss how approaches such as ProxyPrint that are designed aware of the medium can create more engaging making tools that can embed tacit knowledge, encourage creativity, and sustain crafting practice.

Illustrations of human movements are used to communicate ideas and convey instructions in many domains, but creating them is time-consuming and requires skill. We introduce DemoDraw, a multi-modal approach to generate these illustrations as the user physically demonstrates the movements. In a Demonstration Interface, DemoDraw segments speech and 3D joint motion into a sequence of motion segments, each characterized by a key pose and salient joint trajectories. Based on this sequence, a series of illustrations is automatically generated using a stylistically rendered 3D avatar annotated with arrows to convey movements. During demonstration, the user can navigate using speech and amend or re-perform motions if needed. Once a suitable sequence of steps has been created, a Refinement Interface enables fine control of visualization parameters. In a three-part evaluation, we validate the effectiveness of the generated illustrations and the usability of DemoDraw. Our results show 4 to 7-step illustrations can be created in 5 or 10 minutes on average.

As interactive electronics become increasingly intimate and personal, the design of circuitry is correspondingly developing a more playful and creative aesthetic. Circuit sketching and design is a multidimensional activity which combines the arts, crafts, and engineering broadening participation of electronic creation to include makers of diverse backgrounds. In order to support this design ecology, we present Ellustrate, a digital design tool that enables the functional and aesthetic design of electronic circuits with multiple conductive and dielectric materials. Ellustrate guides users through the fabrication and debugging process, easing the task of practical circuit creation while supporting designers' aesthetic decisions throughout the circuit authoring workflow. In a formal user study, we demonstrate how Ellustrate enables a new electronic design conversation that combines electronics, materials, and visual aesthetic concerns.

Speech recordings are central to modern media from podcasts to audio books to e-lectures and voice-overs. Authoring these recordings involves an iterative back and forth process between script writing/editing and audio recording/editing. Yet, most existing tools treat the script and the audio separately, making the back and forth workflow very tedious. We present Voice Script, an interface to support a dynamic workflow for script writing and audio recording/editing. Our system integrates the script with the audio such that, as the user writes the script or records speech, edits to the script are translated to the audio and vice versa. Through informal user studies, we demonstrate that our interface greatly facilitates the audio authoring process in various scenarios.

We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.

Performance-driven character animation enables users to create expressive results by performing the desired motion of the character with their face and/or body. However, for cutout animations where continuous motion is combined with discrete artwork replacements, supporting a performance-driven workflow has some unique requirements. To trigger the appropriate artwork replacements, the system must reliably detect a wide range of customized facial expressions that are challenging for existing recognition methods, which focus on a few canonical expressions (e.g., angry, disgusted, scared, happy, sad and surprised). Also, real usage scenarios require the system to work in realtime with minimal training. In this paper, we propose a novel customized expression recognition technique that meets all of these requirements. We first use a set of handcrafted features combining geometric features derived from facial landmarks and patch-based appearance features through group sparsity-based facial component learning. To improve discrimination and generalization, these handcrafted features are integrated into a custom-designed Deep Convolutional Neural Network (CNN) structure trained from publicly available facial expression datasets. The combined features are fed to an online ensemble of SVMs designed for the few training sample problem and performs in realtime. To improve temporal coherence, we also apply a Hidden Markov Model (HMM) to smooth the recognition results. Our system achieves state-of-the-art performance on canonical expression datasets and promising results on our collected dataset of customized expressions.

Patterns play a central role in 2D graphic design. A critical step in the design of patterns is evaluating multiple design alternatives. Exploring these alternatives with existing tools is challenging because most tools force users to work with a single fixed representation of the pattern that encodes a specific set of geometric relationships between pattern elements. However, for most patterns, there are many different interpretations of its regularity that correspond to different design variations. The exponential nature of this variation space makes the problem of finding all variations intractable. We present a method called PATEX to characterize and efficiently identify distinct and valid pattern variations, allowing users to directly navigate the variation space. Technically, we propose a novel linear approximation to handle the complexity of the problem and efficiently enumerate suitable pattern variations under proposed element movements. We also present two pattern editing interfaces that expose the detected pattern variations as suggested edits to the user. We show a diverse collection of pattern edits and variations created with PATEX. The results from our user study indicate that our suggested variations can be useful and inspirational for typical pattern editing tasks.

Creating personal narratives helps people build meaning around their experiences. However, novices lack the knowledge and experience to create stories with strong narrative structure. Current storytelling tools often structure novice work through templates, enforcing a linear creative process that asks novices for materials they may not have. In this paper, we propose scaffolding creative work using storytelling patterns extracted from stories created by experts. Patterns are modular sets of related camera shots that expert videographers commonly use to achieve a specific narrative function. After identifying a set of patterns from high-quality storytelling videos, we created Motif, a mobile video storytelling application that allows users to construct video stories by combining these patterns. By making existing solutions used by experts available to novices, we encourage capturing shots with story structure and narrative goals in mind. In a controlled study where we asked participants to create travel video stories, videos created with patterns conveyed stronger narrative structure and were considered higher quality by expert evaluators than videos created without patterns.

We describe Lamello, an approach for creating tangible input components that recognize user interaction via passive acoustic sensing. Lamello employs comb-like structures with varying-length tines at interaction points (e.g., along slider paths). Moving a component generates tine strikes; a real-time audio processing pipeline analyzes the resultant sounds and emits high-level interaction events. Our main contributions are in the co-design of the tine structures, information encoding schemes, and audio analysis. We demonstrate 3D printed Lamello-powered buttons, sliders, and dials.

Abstract Increasingly, companies are creating product advertisements and catalog images using computer renderings of 3D scenes. A common goal for these companies is to create aesthetically appealing compositions that highlight objects of interest within the context of a scene. Unfortunately, this goal is challenging, not only due to the need to balance the trade‐off among aesthetic principles and design constraints, but also because of the huge search space induced by possible camera parameters, object placement, material choices, etc. Previous methods have investigated only optimization of camera parameters. In this paper, we develop a tool that starts from an initial scene description and a set of high‐level constraints provided by a stylist and then automatically generates an optimized scene whose 2D composition is improved. It does so by locally adjusting the 3D object transformations, surface materials, and camera parameters. The value of this tool is demonstrated in a variety of applications motivated by product catalogs, including rough layout refinement, detail image creation, home planning, cultural customization, and text inlay placement. Results of a perceptual study indicate that our system produces images preferable for product advertisement compared to a more traditional camera‐only optimization.

Abstract Traditional methods for creating dynamic objects and characters from static drawings involve careful tweaking of animation curves and/or simulation parameters. Sprite sheets offer a more drawing‐centric solution, but they do not encode timing information or the logic that determines how objects should transition between poses and cannot generalize outside the given drawings. We present an approach for creating dynamic sprites that leverages sprite sheets while addressing these limitations. In our system, artists create a drawing, deform it to specify a small number of example poses, and indicate which poses can be interpolated. To make the object move, we design a procedural simulation to navigate the pose manifold in response to external or user‐controlled forces. Powerful artistic control is achieved by allowing the artist to specify both the pose manifold and how it is navigated, while physics is leveraged to provide timing and generality. We used our method to create sprites with a range of different dynamic properties. Copyright © 2014 John Wiley & Sons, Ltd.

Wire wrapping is a traditional form of handmade jewelry that involves bending metal wire to create intricate shapes. The technique appeals to novices and casual crafters because of its low cost, accessibility and unique aesthetic. We present a computational design tool that addresses the two main challenges of creating 2D wire-wrapped jewelry: decomposing an input drawing into a set of wires, and bending the wires to give them shape. Our main contribution is an automatic wire decomposition algorithm that segments a drawing into a small number of wires based on aesthetic and fabrication principles. We formulate the task as a constrained graph labeling problem and present a stochastic optimization approach that produces good results for a variety of inputs. Given a decomposition, our system generates a 3D-printed custom support structure, or jig , that helps users bend the wire into the appropriate shape. We validated our wire decomposition algorithm against existing wire-wrapped designs, and used our end-to-end system to create new jewelry from clipart drawings. We also evaluated our approach with novice users, who were able to create various pieces of jewelry in less than half an hour.

This paper presents a method for learning to predict the stylistic compatibility between 3D furniture models from different object classes: e.g., how well does this chair go with that table? To do this, we collect relative assessments of style compatibility using crowdsourcing. We then compute geometric features for each 3D model and learn a mapping of them into a space where Euclidean distances represent style incompatibility. Motivated by the geometric subtleties of style, we introduce part-aware geometric feature vectors that characterize the shapes of different parts of an object separately. Motivated by the need to compute style compatibility between different object classes, we introduce a method to learn object class-specific mappings from geometric features to a shared feature space. During experiments with these methods, we find that they are effective at predicting style compatibility agreed upon by people. We find in user studies that the learned compatibility metric is useful for novel interactive tools that: 1) retrieve stylistically compatible models for a query, 2) suggest a piece of furniture for an existing scene, and 3) help guide an interactive 3D modeler towards scenes with compatible furniture.

Good icons are legible, and legible icons are scale-dependent. Experienced icon designers use a set of common strategies to create legible scale variations of icons, but executing those strategies with current tools can be challenging. In part, this is because many apparent objects, like hairlines formed by negative space, are not explicitly represented as objects in vector drawings. We present transient widgets as a mechanism for selecting and manipulating apparent objects that is independent of the underlying drawing representation. We implement transient widgets using a constraint-based editing framework; demonstrate their utility for performing the kinds of edits most common when producing scale variations of icons; and report qualitative feedback on the system from professional icon designers.

Blackboard-style lecture videos are popular, but learning using existing video player interfaces can be challenging. Viewers cannot consume the lecture material at their own pace, and the content is also difficult to search or skim. For these reasons, some people prefer lecture notes to videos. To address these limitations, we present Visual Transcripts , a readable representation of lecture videos that combines visual information with transcript text. To generate a Visual Transcript, we first segment the visual content of a lecture into discrete visual entities that correspond to equations, figures, or lines of text. Then, we analyze the temporal correspondence between the transcript and visuals to determine how sentences relate to visual entities. Finally, we arrange the text and visuals in a linear layout based on these relationships. We compare our result with a standard video player, and a state-of-the-art interface designed specifically for blackboard-style lecture videos. User evaluation suggests that users prefer our interface for learning and that our interface is effective in helping them browse or search through lecture videos.

We present PortraitSketch, an interactive drawing system that helps novices create pleasing, recognizable face sketches without requiring prior artistic training. As the user traces over a source portrait photograph, PortraitSketch automatically adjusts the geometry and stroke parameters (thickness, opacity, etc.) to improve the aesthetic quality of the sketch. We present algorithms for adjusting both outlines and shading strokes based on important features of the underlying source image. In contrast to automatic stylization systems, PortraitSketch is designed to encourage a sense of ownership and accomplishment in the user. To this end, all adjustments are performed in real-time, and the user ends up directly drawing all strokes on the canvas. The findings from our user study suggest that users prefer drawing with some automatic assistance, thereby producing better drawings, and that assistance does not decrease the perceived level of involvement in the creative process.

Designers often create physical works-like prototypes early in the product development cycle to explore possible mechanical architectures for a design. Yet, creating functional prototypes requires time and expertise, which discourages rapid design iterations. Designers must carefully specify part and joint parameters to ensure that parts move and fit and together in the intended manner. We present an interactive system that streamlines the process by allowing users to annotate rough 3D models with high-level functional relationships (e.g., part A fits inside part B ). Based on these relationships, our system optimizes the model geometry to produce a working design. We demonstrate the versatility of our system by using it to design a variety of works-like prototypes.

Traditional methods for creating dynamic objects and characters from static drawings involve careful tweaking of animation curves and/or simulation parameters. Sprite sheets offer a more drawing-centric solution, but they do not encode timing information or the logic that determines how objects should transition between poses and cannot generalize outside the given drawings. We present an approach for creating dynamic sprites that leverages sprite sheets while addressing these limitations. In our system, artists create a drawing, deform it to specify a small number of example poses, and indicate which poses can be interpolated. To make the object move, we design a procedural simulation to navigate the pose manifold in response to external or user-controlled forces. Powerful artistic control is achieved by allowing the artist to specify both the pose manifold and how it is navigated, while physics is leveraged to provide timing and generality. We used our method to create sprites with a range of different dynamic properties.

Amateur instructional videos often show a single uninterrupted take of a recorded demonstration without any edits. While easy to produce, such videos are often too long as they include unnecessary or repetitive actions as well as mistakes. We introduce DemoCut, a semi-automatic video editing system that improves the quality of amateur instructional videos for physical tasks. DemoCut asks users to mark key moments in a recorded demonstration using a set of marker types derived from our formative study. Based on these markers, the system uses audio and video analysis to automatically organize the video into meaningful segments and apply appropriate video editing effects. To understand the effectiveness of DemoCut, we report a technical evaluation of seven video tutorials created with DemoCut. In a separate user evaluation, all eight participants successfully created a complete tutorial with a variety of video editing effects using our system.

Audio stories are an engaging form of communication that combine speech and music into compelling narratives. Existing audio editing tools force story producers to manipulate speech and music tracks via tedious, low-level waveform editing. In contrast, we present a set of tools that analyze the audio content of the speech and music and thereby allow producers to work at much higher level. Our tools address several challenges in creating audio stories, including (1) navigating and editing speech, (2) selecting appropriate music for the score, and (3) editing the music to complement the speech. Key features include a transcript-based speech editing tool that automatically propagates edits in the transcript text to the corresponding speech track; a music browser that supports searching based on emotion, tempo, key, or timbral similarity to other songs; and music retargeting tools that make it easy to combine sections of music with the speech. We have used our tools to create audio stories from a variety of raw speech sources, including scripted narratives, interviews and political speeches. Informal feedback from first-time users suggests that our tools are easy to learn and greatly facilitate the process of editing raw footage into a final story.

Mechanical figures that mimic human motions continue to entertain us and capture our imagination. Creating such automata requires expertise in motion planning, knowledge of mechanism design, and familiarity with fabrication constraints. Thus, automaton design remains restricted to only a handful of experts. We propose an automatic algorithm that takes a motion sequence of a humanoid character and generates the design for a mechanical figure that approximates the input motion when driven with a single input crank. Our approach has two stages. The motion approximation stage computes a motion that approximates the input sequence as closely as possible while remaining compatible with the geometric and motion constraints of the mechanical parts in our design. Then, in the layout stage, we solve for the sizing parameters and spatial layout of all the elements, while respecting all fabrication and assembly constraints. We apply our algorithm on a range of input motions taken from motion capture databases. We also fabricate two of our designs to demonstrate the viability of our approach.

As large repositories of 3D shape collections continue to grow, understanding the data, especially encoding the inter-model similarity and their variations, is of central importance. For example, many data-driven approaches now rely on access to semantic segmentation information, accurate inter-model point-to-point correspondence, and deformation models that characterize the model collections. Existing approaches, however, are either supervised requiring manual labeling; or employ super-linear matching algorithms and thus are unsuited for analyzing large collections spanning many thousands of models. We propose an automatic algorithm that starts with an initial template model and then jointly optimizes for part segmentation, point-to-point surface correspondence, and a compact deformation model to best explain the input model collection. As output, the algorithm produces a set of probabilistic part-based templates that groups the original models into clusters of models capturing their styles and variations. We evaluate our algorithm on several standard datasets and demonstrate its scalability by analyzing much larger collections of up to thousands of shapes.

Concept sketches are popularly used by designers to convey pose and function of products. Understanding such sketches, however, requires special skills to form a mental 3D representation of the product geometry by linking parts across the different sketches and imagining the intermediate object configurations. Hence, the sketches can remain inaccessible to many, especially non-designers. We present a system to facilitate easy interpretation and exploration of concept sketches. Starting from crudely specified incomplete geometry, often inconsistent across the different views, we propose a globally-coupled analysis to extract part correspondence and inter-part junction information that best explain the different sketch views. The user can then interactively explore the abstracted object to gain better understanding of the product functions. Our key technical contribution is performing shape analysis without access to any coherent 3D geometric model by reasoning in the space of inter-part relations. We evaluate our system on various concept sketches obtained from popular product design books and websites.

Users of complex software applications often learn concepts and skills through step-by-step tutorials. Today, these tutorials are published in two dominant forms: static tutorials composed of images and text that are easy to scan, but cannot effectively describe dynamic interactions; and video tutorials that show all manipulations in detail, but are hard to navigate. We hypothesize that a mixed tutorial with static instructions and per-step videos can combine the benefits of both formats. We describe a comparative study of static, video, and mixed image manipulation tutorials with 12 participants and distill design guidelines for mixed tutorials. We present MixT, a system that automatically generates step-by-step mixed media tutorials from user demonstrations. MixT segments screencapture video into steps using logs of application commands and input events, applies video compositing techniques to focus on salient infor-mation, and highlights interactions through mouse trails. Informal evaluation suggests that automatically generated mixed media tutorials were as effective in helping users complete tasks as tutorials that were created manually.

Powerful image editing software like Adobe Photoshop and GIMP have complex interfaces that can be hard to master. To help users perform image editing tasks, we introduce tutorial-based applications (tapps) that retain the step-by-step structure and descriptive text of tutorials but can also automatically apply tutorial steps to new images. Thus, tapps can be used to batch process many images automatically, similar to traditional macros. Tapps also support interactive exploration of parameters, automatic variations, and direct manipulation (e.g., selection, brushing). Another key feature of tapps is that they execute on remote instances of Photoshop, which allows users to edit their images on any Web-enabled device. We demonstrate a working prototype system called TappCloud for creating, managing and using tapps. Initial user feedback indicates support for both the interactive features of tapps and their ability to automate image editing. We conclude with a discussion of approaches and challenges of pushing monolithic direct-manipulation GUIs to the cloud.

Audio producers often use musical underlays to emphasize key moments in spoken content and give listeners time to reflect on what was said. Yet, creating such underlays is time-consuming as producers must carefully (1) mark an emphasis point in the speech (2) select music with the appropriate style, (3) align the music with the emphasis point, and (4) adjust dynamics to produce a harmonious composition. We present UnderScore, a set of semi-automated tools designed to facilitate the creation of such underlays. The producer simply marks an emphasis point in the speech and selects a music track. UnderScore automatically refines, aligns and adjusts the speech and music to generate a high-quality underlay. UnderScore allows producers to focus on the high-level design of the underlay; they can quickly try out a variety of music and test different points of emphasis in the story. Amateur producers, who may lack the time or skills necessary to author underlays, can quickly add music to their stories. An informal evaluation of UnderScore suggests that it can produce high-quality underlays for a variety of examples while significantly reducing the time and effort required of radio producers.

We present a set of tools designed to help editors place cuts and create transitions in interview video. To help place cuts, our interface links a text transcript of the video to the corresponding locations in the raw footage. It also visualizes the suitability of cut locations by analyzing the audio/visual features of the raw footage to find frames where the speaker is relatively quiet and still. With these tools editors can directly highlight segments of text, check if the endpoints are suitable cut locations and if so, simply delete the text to make the edit. For each cut our system generates visible (e.g. jump-cut, fade, etc.) and seamless, hidden transitions. We present a hierarchical, graph-based algorithm for efficiently generating hidden transitions that considers visual features specific to interview footage. We also describe a new data-driven technique for setting the timing of the hidden transition. Finally, our tools offer a one click method for seamlessly removing 'ums' and repeated words as well as inserting natural-looking pauses to emphasize semantic content. We apply our tools to edit a variety of interviews and also show how they can be used to quickly compose multiple takes of an actor narrating a story.

Large collections of 3D models from the same object class (e.g., chairs, cars, animals) are now commonly available via many public repositories, but exploring the range of shape variations across such collections remains a challenging task. In this work, we present a new exploration interface that allows users to browse collections based on similarities and differences between shapes in user-specified regions of interest (ROIs). To support this interactive system, we introduce a novel analysis method for computing similarity relationships between points on 3D shapes across a collection. We encode the inherent ambiguity in these relationships using fuzzy point correspondences and propose a robust and efficient computational framework that estimates fuzzy correspondences using only a sparse set of pairwise model alignments. We evaluate our analysis method on a range of correspondence benchmarks and report substantial improvements in both speed and accuracy over existing alternatives. In addition, we demonstrate how fuzzy correspondences enable key features in our exploration tool, such as automated view alignment, ROI-based similarity search, and faceted browsing.

Video tutorials provide a convenient means for novices to learn new software applications. Unfortunately, staying in sync with a video while trying to use the target application at the same time requires users to repeatedly switch from the application to the video to pause or scrub backwards to replay missed steps. We present Pause-and-Play, a system that helps users work along with existing video tutorials. Pause-and-Play detects important events in the video and links them with corresponding events in the target application as the user tries to replicate the depicted procedure. This linking allows our system to automatically pause and play the video to stay in sync with the user. Pause-and-Play also supports convenient video navigation controls that are accessible from within the target application and allow the user to easily replay portions of the video without switching focus out of the application. Finally, since our system uses computer vision to detect events in existing videos and leverages application scripting APIs to obtain real time usage traces, our approach is largely independent of the specific target application and does not require access or modifications to application source code. We have implemented Pause-and-Play for two target applications, Google SketchUp and Adobe Photoshop, and we report on a user study that shows our system improves the user experience of working with video tutorials.

We present a framework for generating content-adaptive macros that can transfer complex photo manipulations to new target images. We demonstrate applications of our framework to face, landscape, and global manipulations. To create a content-adaptive macro, we make use of multiple training demonstrations. Specifically, we use automated image labeling and machine learning techniques to learn the dependencies between image features and the parameters of each selection, brush stroke, and image processing operation in the macro. Although our approach is limited to learning manipulations where there is a direct dependency between image features and operation parameters, we show that our framework is able to learn a large class of the most commonly used manipulations using as few as 20 training demonstrations. Our framework also provides interactive controls to help macro authors and users generate training demonstrations and correct errors due to incorrect labeling or poor parameter estimation. We ask viewers to compare images generated using our content-adaptive macros with and without corrections to manually generated ground-truth images and find that they consistently rate both our automatic and corrected results as close in appearance to the ground truth. We also evaluate the utility of our proposed macro generation workflow via a small informal lab study with professional photographers. The study suggests that our workflow is effective and practical in the context of real-world photo editing.

As large public repositories of 3D shapes continue to grow, the amount of shape variability in such collections also increases, both in terms of the number of different classes of shapes, as well as the geometric variability of shapes within each class. While this gives users more choice for shape selection, it can be difficult to explore large collections and understand the range of variations amongst the shapes. Exploration is particularly challenging for public shape repositories, which are often only loosely tagged and contain neither point-based nor part-based correspondences. In this paper, we present a method for discovering and exploring continuous variability in a collection of 3D shapes without correspondences. Our method is based on a novel navigation interface that allows users to explore a collection of related shapes by deforming a base template shape through a set of intuitive deformation controls. We also help the user to select the most meaningful deformations using a novel technique for learning shape variability in terms of deformations of the template. Our technique assumes that the set of shapes lies near a low-dimensional manifold in a certain descriptor space, which allows us to avoid establishing correspondences between shapes, while being rotation and scaling invariant. We present results on several shape collections taken directly from public repositories.

To create collections, like music playlists from personal media libraries, users today typically do one of two things. They either manually select items one-by-one, which can be time consuming, or they use an example-based recommendation system to automatically generate a collection. While such automatic engines are convenient, they offer the user limited control over how items are selected. Based on prior research and our own observations of existing practices, we propose a semi-automatic interface for creating collections that combines automatic suggestions with manual refinement tools. Our system includes a keyword query interface for specifying high-level collection preferences (e.g., "some rock, no Madonna, lots of U2,") as well as three example-based collection refinement techniques: 1) a suggestion widget for adding new items in-place in the context of the collection; 2) a mechanism for exploring alternatives for one or more collection items; and 3) a two-pane linked interface that helps users browse their libraries based on any selected collection item. We demonstrate our approach with two applications. SongSelect helps users create music playlists, and PhotoSelect helps users select photos for sharing. Initial user feedback is positive and confirms the need for semi-automated tools that give users control over automatically created collections.

How things workvisualizations use a variety of visual techniques to depict the operation of complex mechanical assemblies. We present an automated approach for generating such visualizations. Starting with a 3D CAD model of an assembly, we first infer the motions of individual parts and the interactions between parts based on their geometry and a few user specified constraints. We then use this information to generate visualizations that incorporate motion arrows, frame sequences and animation to convey the causal chain of motions and mechanical interactions between parts. We present results for a wide variety of assemblies.

We present a technique for visualizing complicated mathematical surfaces that is inspired by hand-designed topological illustrations. Our approach generates exploded views that expose the internal structure of such a surface by partitioning it into parallel slices, which are separated from each other along a single linear explosion axis. Our contributions include a set of simple, prescriptive design rules for choosing an explosion axis and placing cutting planes, as well as automatic algorithms for applying these rules. First we analyze the input shape to select the explosion axis based on the detected rotational and reflective symmetries of the input model. We then partition the shape into slices that are designed to help viewers better understand how the shape of the surface and its cross-sections vary along the explosion axis. Our algorithms work directly on triangle meshes, and do not depend on any specific parameterization of the surface. We generate exploded views for a variety of mathematical surfaces using our system.

We review the literature on automatic document formatting with an emphasis on recent work in the field. One common way to frame document formatting is as a constrained optimization problem where decision variables encode element placement, constraints enforce required geometric relationships, and the objective function measures layout quality. We present existing research using this framework, describing the kind of optimization problem being solved and the basic optimization techniques used to solve it. Our review focuses on the formatting of primarily textual documents, including both micro- and macro-typographic concerns. We also cover techniques for automatic table layout. Related problems such as widget and diagram layout, as well as temporal layout issues that arise in multimedia documents are outside the scope of this review.

We present a demonstration-based system for automatically generating succinct step-by-step visual tutorials of photo manipulations. An author first demonstrates the manipulation using an instrumented version of GIMP that records all changes in interface and application state. From the example recording, our system automatically generates tutorials that illustrate the manipulation using images, text, and annotations. It leverages automated image labeling (recognition of facial features and outdoor scene structures in our implementation) to generate more precise text descriptions of many of the steps in the tutorials. A user study comparing our automatically generated tutorials to hand-designed tutorials and screen-capture video recordings finds that users are 20--44% faster and make 60--95% fewer errors using our tutorials. While our system focuses on tutorial generation, we also present some initial work on generating content-dependent macros that use image recognition to automatically transfer selection operations from the example image used in the demonstration to new target images. While our macros are limited to transferring selection operations we demonstrate automatic transfer of several common retouching techniques including eye recoloring, whitening teeth and sunset enhancement.

We present a system for creating and viewing interactive exploded views of complex 3D models. In our approach, a 3D input model is organized into an explosion graph that encodes how parts explode with respect to each other. We present an automatic method for computing explosion graphs that takes into account part hierarchies in the input models and handles common classes of interlocking parts. Our system also includes an interface that allows users to interactively explore our exploded views using both direct controls and higher-level interaction modes.

We present a system for authoring and viewing interactive cutaway illustrations of complex 3D models using conventions of traditional scientific and technical illustration. Our approach is based on the two key ideas that 1) cuts should respect the geometry of the parts being cut, and 2) cutaway illustrations should support interactive exploration. In our approach, an author instruments a 3D model with auxiliary parameters, which we call "rigging," that define how cutaways of that structure are formed. We provide an authoring interface that automates most of the rigging process. We also provide a viewing interface that allows viewers to explore rigged models using high-level interactions. In particular, the viewer can just select a set of target structures, and the system will automatically generate a cutaway illustration that exposes those parts. We have tested our system on a variety of CAD and anatomical models, and our results demonstrate that our approach can be used to create and view effective interactive cutaway illustrations for a variety of complex objects with little user effort.

Grid-based page designs are ubiquitous in commercially printed publications, such as newspapers and magazines. Yet, to date, no one has invented a good way to easily and automatically adapt such designs to arbitrarily-sized electronic displays. The difficulty of generalizing grid-based designs explains the generally inferior nature of on-screen layouts when compared to their printed counterparts, and is arguably one of the greatest remaining impediments to creating on-line reading experiences that rival those of ink on paper. In this work, we present a new approach to adaptive grid-based document layout, which attempts to bridge this gap. In our approach, an adaptive layout style is encoded as a set of grid-based templates that know how to adapt to a range of page sizes and other viewing conditions. These templates include various types of layout elements (such as text, figures, etc.) and define, through constraint-based relationships, just how these elements are to be laid out together as a function of both the properties of the content itself, such as a figure's size and aspect ratio, and the properties of the viewing conditions under which the content is being displayed. We describe an XML-based representation for our templates and content, which maintains a clean separation between the two. We also describe the various parts of our research prototype system: a layout engine for formatting the page; a paginator for determining a globally optimal allocation of content amongst the pages, as well as an optimal pairing of templates with content; and a graphical user interface for interactively creating adaptive templates. We also provide numerous examples demonstrating the capabilities of this prototype, including this paper, itself, which has been laid out with our system.

We describe a system for non-photorealistic rendering (NPR) of virtual environments. In real time, it synthesizes imagery of architectural interiors using stroke-based textures. We address the four main challenges of such a system — interactivity, visual detail, controlled stroke size, and frame-to-frame coherence — through image based rendering (IBR) methods. In a preprocessing stage, we capture photos of a real or synthetic environment, map the photos to a coarse model of the environment, and run a series of NPR filters to generate textures. At runtime, the system re-renders the NPR textures over the geometry of the coarse model, and it adds dark lines that emphasize creases and silhouettes. We provide a method for constructing non-photorealistic textures from photographs that largely avoids seams in the resulting imagery. We also offer a new construction, art-maps, to control stroke size across the images. Finally, we show a working system that provides an immersive experience rendered in a variety of NPR styles.