Creating 3D character animations traditionally requires significant time and effort from the animator. Advancements in generative methods now enable easy creation of multiple character animation variations for use or further editing. However, this capability introduces a new challenge in comparing character animations to select the best animation, which is challenging due to temporal misalignment and the large amount of spatial data. We present AnimationDiff, a visual comparison tool for generated character animations. AnimationDiff enables contextual comparisons in the intended scene and camera angle, and embedding of spatial information by combining established animation visualization techniques and easy switching between overlaid and side-by-side comparisons. AnimationDiff also supports filtering to handle information overload, and Temporal Lenses that visualize entire animations over time for overview, alignment, and comparison. We evaluated AnimationDiff in a user study, showcasing its efficacy in animation comparison and providing design insights for comparing motion.

As an emergent process, creativity relies on explorations via sampling and prototyping for problem construction. These activities compile knowledge, provide a context enveloping the solution, and answer questions. With Generative AI, practitioners can go beyond sampling existing media towards instantly generating and remixing new ones. We refer to this convergence as ’protosampling’. Using existing literature we ground a definition for protosampling and operationalize it through Atelier, a canvas-like system that leverages a variety of generative image and video models for visual creation. Atelier: (1) blends the spaces for thinking and creation, where both references and generated assets co-exist in one space, (2) provides various encapsulated technical workflows that focus on the activity at hand, and (3) enables navigating emergence through interactive visualizations, smart search, and collections. Protosampling as a lens reframes creative work to emphasize the process itself and how seemingly disjointed thoughts can tightly interweave into a final solution.

In pre-production, filmmakers and 3D animation experts must rapidly prototype ideas to explore a film’s possibilities before full-scale production, yet conventional approaches involve trade-offs in efficiency and expressiveness. Hand-drawn storyboards often lack spatial precision needed for complex cinematography, while 3D previsualization demands expertise and high-quality rigged assets. To address this gap, we present PrevizWhiz, a system that leverages rough 3D scenes in combination with generative image and video models to create stylized video previews. The workflow integrates frame-level image restyling with adjustable resemblance, time-based editing through motion paths or external video inputs, and refinement into high-fidelity video clips. A study with filmmakers demonstrates that our system lowers technical barriers for film-makers, accelerates creative iteration, and effectively bridges the communication gap, while also surfacing challenges of continuity, authorship, and ethical consideration in AI-assisted filmmaking.

Virtual Production (VP) professionals often face challenges accessing tacit knowledge and creative intent, which are important in forming common ground with collaborators and in contributing more effectively and efficiently to the team. From our formative study (N=23) with a follow-up interview (N=6), we identified the significance and prevalence of this challenge. To help professionals access knowledge, we present GroundLink, a Unity add-on that surfaces meeting-derived knowledge directly in the editor to support establishing common ground. It features a meeting knowledge dashboard for capturing and reviewing decisions and comments, constraint-aware feedforward that proactively informs the editor environment, and cross-modal synchronization that provides referential links between the dashboard and the editor. A comparative study (N=12) suggested that GroundLink help users build common ground with their team while improving perceived confidence and ease of editing the 3D scene. An expert evaluation with VP professionals (N=5) indicated strong potential for GroundLink in real-world workflows.

Current AI writing tools, which rely on text prompts, poorly support the spatial and interactive nature of storytelling where ideas emerge from direct manipulation and play. We present PlayWrite, a mixed-reality system where users author stories by directly manipulating virtual characters and props. A multi-agent AI pipeline interprets these actions into Intent Frames—structured narrative beats visualized as rearrangeable story marbles on a timeline. A large language model then transforms the user’s assembled sequence into a final narrative. A user study (N=13) with writers from varying domains found that PlayWrite fosters a highly improvisational and playful process. Users treated the AI as a collaborative partner, using its unexpected responses to spark new ideas and overcome creative blocks. PlayWrite demonstrates an approach for co-creative systems that move beyond text to embrace direct manipulation and play as core interaction modalities.

Joanne Leong, David Ledo, Thomas Driscoll, Tovi Grossman, George Fitzmaurice, and Fraser Anderson. 2025. Paratrouper: Exploratory Creation of Character Cast Visuals Using Generative AI. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 189, 1–20.

Millions of individuals with diabetes use glucose monitors to track blood sugar levels. Research shows that such individuals seek to customize different aspects of their interactions with these devices, including how they engage with, decorate, and wear them. However, it remains challenging to tailor both device form and function to accommodate individual needs. To address this challenge, we introduce GlucoMaker, a system for collaboratively customizing physical design aspects of glucose monitors. Prior to designing GlucoMaker, we conducted a prototyping and focus group study to understand customization preferences and collaboration benefits. GlucoMaker provides individuals with the ability to a) select monitor form and function preferences, b) alter predefined and downloadable digital model files, c) receive feedback on monitor designs from stakeholders, and d) learn technical design aspects. We further demonstrate the versatility and design space of GlucoMaker with three examples of different form and function use cases.

Cross-reality tasks, like creating or consuming virtual reality (VR) content, often involve inconvenient or distracting switches between desktop and VR. An initial formative study explores cross-reality switching habits, finding most switches are momentary “peeks” between interfaces, with specific habits determined by current context. The results inform a design space for context-aware “peeking” techniques that allow users to view or interact with desktop from VR, and vice versa, without fully switching. We implemented a set of peeking techniques and evaluated them in two levels of a cross-reality task: one requiring only viewing, and another requiring input and viewing. Peeking techniques made task completion faster, with increased input accuracy and reduced perceived workload.

Editing character motion in Virtual Reality is challenging as it requires working with both spatial and temporal data using controls with multiple degrees-of-freedom. The spatial and temporal controls are separated, making it difficult to adjust poses over time and predict the effects across adjacent frames. To address this challenge, we propose TimeTunnel, an immersive motion editing interface that integrates spatial and temporal control for 3D character animation in VR. TimeTunnel provides an approachable editing experience via KeyPoses and Trajectories. KeyPoses are a set of representative poses automatically computed to concisely depict motion. Trajectories are 3D animation curves that pass through the joints of KeyPoses to represent in-betweens. TimeTunnel integrates spatial and temporal control by superimposing Trajectories and KeyPoses onto a 3D character. We conducted two studies to evaluate TimeTunnel. In our quantitative study, TimeTunnel reduced the amount of time required for editing motion, and saved effort in locating target poses. Our qualitative study with domain experts demonstrated how TimeTunnel is an approachable interface that can simplify motion editing, while still preserving a direct representation of motion.

Creative practitioners rely on sampling to understand, explore, and construct problems; or gather resources for later use. Despite practitioners’ ability to experience immersive environments, sampling from them remains limited to primarily visual captures (e.g., screenshots, videos), which overlook the richness and variety of available media. To address these challenges, we describe “Immersive Sampling” as a new way to frame information gathering in the context of immersive environments. In the context of Immersive Sampling, practitioners engage in experiencing immersive environments while capturing, organizing, revisiting, and remixing found content. We situate this subset of tasks in literature and argue for their importance for emerging, future content creation domains. To further explore how Immersive Sampling might take place, we created VRicolage, a proof-of-concept prototype showcasing a set of interactions in Virtual Reality to sample, revisit, and remix captures. Given the democratization of immersive environments, Immersive Sampling provides practitioners with a means to collect, revisit, and remix digital materials.

New immersive 3D design tools enable the creation of spatial design recordings, capturing collaborative design activities. By reviewing captured spatial design sessions, which include user activities, workflows, and tool use, users can reflect on their own design processes, learn new workflows, and understand others’ design rationale. However, finding interesting moments in design activities can be challenging: they contain multimodal data (such as user motion and logged events) occurring over time which can be difficult to specify when searching, and are typically distributed over many sessions or recordings. We present Tesseract, a Worlds-in-Miniature-based system to expressively query VR spatial design recordings. Tesseract consists of the Search Cube interface acting as a centralized stage-to-search container, and four querying tools for specifying multimodal data to enable users to find interesting moments in past design activities. We studied ten participants who used Tesseract and found support for our miniature-based stage-to-search approach.

Crafting a rich and unique environment is crucial for fictional world-building, but can be difficult to achieve since illustrating a world from scratch requires time and significant skill. We investigate the use of recent multi-modal image generation systems to enable users iteratively visualize and modify elements of their fictional world using a combination of text input, sketching, and region-based filling. WorldSmith enables novice world builders to quickly visualize a fictional world with layered edits and hierarchical compositions. Through a formative study (4 participants) and first-use study (13 participants) we demonstrate that WorldSmith offers more expressive interactions with prompt-based models. With this work, we explore how creatives can be empowered to leverage prompt-based generative AI as a tool in their creative process, beyond current "click-once" prompting UI paradigms.

In early stages of creative processes, practitioners externalize and combine inspirational materials, using strategies such as mood board creation to achieve a desired vision and aesthetic. Yet, collecting and combining materials can be difficult: (1) mood boards bias towards 2D images, neglecting audio, video, and 3D models; (2) alternative externalizations such as prototypes are best suited for later stages and can be time-consuming and tedious to create; and (3) online searches lead to disjointed sources between different websites and assets in the file system. To address these challenges, we created MoodCubes, a system for rapid creation and manipulation of multimedia content. When adding content, MoodCubes decomposes objects (e.g., extracting colour palettes), suggests new materials without the need to search (e.g., 3D models, images, lighting effects), and provides filters to change the scene’s aesthetic. We studied eight creative professionals using MoodCubes, which suggested ways the system might advance existing design practices.

With the rapid development of creativity support tools, creative practitioners (e.g., designers, artists, architects) have to constantly explore and adopt new tools into their practice. While HCI research has focused on developing novel creativity support tools, little is known about creative practitioner’s values when exploring and adopting these tools. We collect and analyze 23 videos, 13 interviews, and 105 survey responses of creative practitioners reflecting on their values to derive a value framework. We find that practitioners value the tools’ functionality, integration into their current workflow, performance, user interface and experience, learning support, costs and emotional connection, in that order. They largely discover tools through personal recommendations. To help unify and encourage reflection from the wider community of CST stakeholders (e.g., systems creators, researchers, marketers, educators), we situate the framework within existing research on systems, creativity support tools and technology adoption.

Analysis of human motion data can reveal valuable insights about the utilization of space and interaction of humans with their environment. To support this, we present AvatAR, an immersive analysis environment for the in-situ visualization of human motion data, that combines 3D trajectories with virtual avatars showing people’s detailed movement and posture. Additionally, we describe how visualizations can be embedded directly into the environment, showing what a person looked at or what surfaces they touched, and how the avatar’s body parts can be used to access and manipulate those visualizations. AvatAR combines an AR HMD with a tablet to provide both mid-air and touch interaction for system control, as well as an additional overview device to help users navigate the environment. We implemented a prototype and present several scenarios to show that AvatAR can enhance the analysis of human motion data by making data not only explorable, but experienceable.

Virtual Reality (VR) has potential for productive knowledge work, however, midair pointing with controllers or hand gestures does not offer the precision and comfort of traditional 2D mice. Directly integrating mice into VR is difficult as selecting targets in a 3D space is negatively impacted by binocular rivalry, perspective mismatch, and improperly calibrated control-display (CD) gain. To address these issues, we developed Depth-Adaptive Cursor , a 2D-mouse driven pointing technique for 3D selection with depth-adaptation that continuously interpolates the cursor depth by inferring what users intend to select based on the cursor position, the viewpoint, and the selectable objects. Depth-Adaptive Cursor uses a novel CD gain tool to compute a usable range of CD gains for general mouse-based pointing in VR. A user study demonstrated that Depth-Adaptive Cursor significantly improved performance compared with an existing mouse-based pointing technique without depth-adaption in terms of time (21.2%), error (48.3%), perceived workload, and user satisfaction.

User modeling is crucial to understanding user behavior and essential for improving user experience and personalized recommendations. When users interact with software, vast amounts of command sequences are generated through logging and analytics systems. These command sequences contain clues to the users’ goals and intents. However, these data modalities are highly unstructured and unlabeled, making it difficult for standard predictive systems to learn from. We propose SimCURL, a simple yet effective contrastive self-supervised deep learning framework that learns user representation from unlabeled command sequences. Our method introduces a user-session network architecture, as well as session dropout as a novel way of data augmentation. We train and evaluate our method on a real-world command sequence dataset of more than half a billion commands. Our method shows significant improvement over existing methods when the learned representation is transferred to downstream tasks such as experience and expertise classification.

We present VideoPoseVR, a video-based animation authoring workflow using online videos to author character animations in VR. It leverages the state-of-the-art deep learning approach to reconstruct 3D motions from online videos, caption the motions, and store them in a motion dataset. Creators can import the videos, search in the dataset, modify the motion timeline, and combine multiple motions from videos to author character animations in VR. We implemented a proof-of-concept prototype and conducted a user study to evaluate the feasibility of the video-based authoring approach as well as gather initial feedback of the prototype. The study results suggest that VideoPoseVR was easy to learn for novice users to author animations and enable rapid exploration of prototyping for applications such as entertainment, skills training, and crowd simulations.

Co-creative AI tools provide a method of creative collaboration between a user and machine. One form of co-creative AI called generative design requires the user to input design parameters and wait substantial periods of time while the system computes design solutions. We explore this interaction dynamic by providing an embodied experience in VR. Calliope is a virtual reality (VR) system that enables users to explore and manipulate generative design solutions in real time. Calliope accounts for the typical idle times in the generative design process by using a virtual environment to encourage parallelized and embodied data-exploration and synthesis, while maintaining a tight human-in-the-loop collaboration with the underlying algorithms. In this paper we discuss design considerations informed by formative studies with generative designers and artists and provide design guidelines to aid others in the development of co-creative AI systems in virtual environments.

Documentation for DIY tasks serve as codified project knowledge and help makers reach new understandings and appreciations for the artifact. Engaging in reflective processes using the documentation can be challenging when it comes to physical objects as the documentation and the artifact exist separately. We hypothesize that spatially associating the documentation information with the artifact can provide richer contextualization to reflect upon the artifact and design process. We implemented and evaluated Documented, a web application that helps makers associate documentation to 3D printed objects. Information can be embedded using printed tags spatially placed on the model and accessed using mobile AR. Our study highlights the different strategies participants had for organizing, embedding, and retrieving information. Informed by our results, we discuss how the coupling of the documentation and the artifact can support reflection and identify potential barriers that need further investigation.

When users complete tasks on the computer, the knowledge they leverage and their intent is often lost because it is tedious or challenging to capture. This makes it harder to understand why a colleague designed a component a certain way or to remember requirements for software you wrote a year ago. We introduce think-aloud computing, a novel application of the think-aloud protocol where computer users are encouraged to speak while working to capture rich knowledge with relatively low effort. Through a formative study we find people shared information about design intent, work processes, problems encountered, to-do items, and other useful information. We developed a prototype that supports think-aloud computing by prompting users to speak and contextualizing speech with labels and application context. Our evaluation shows more subtle design decisions and process explanations were captured in think-aloud than via traditional documentation. Participants reported that think-aloud required similar effort as traditional documentation.

Collaborative visual analytics leverages social interaction to support data exploration and sensemaking. These processes are typically imagined as formalised, extended activities, between groups of dedicated experts, requiring expertise with sophisticated data analysis tools. However, there are many professional domains that benefit from support for short 'bursts' of data exploration between a subset of stakeholders with a diverse breadth of knowledge. Such 'casual collaborative' scenarios will require engaging features to draw users' attention, with intuitive, 'walk-up and use' interfaces. This paper presents Uplift, a novel prototype system to support 'casual collaborative visual analytics' for a campus microgrid, co-designed with local stakeholders. An elicitation workshop with key members of the building management team revealed relevant knowledge is distributed among multiple experts in their team, each using bespoke analysis tools. Uplift combines an engaging 3D model on a central tabletop display with intuitive tangible interaction, as well as augmented-reality, mid-air data visualisation, in order to support casual collaborative visual analytics for this complex domain. Evaluations with expert stakeholders from the building management and energy domains were conducted during and following our prototype development and indicate that Uplift is successful as an engaging backdrop for casual collaboration. Experts see high potential in such a system to bring together diverse knowledge holders and reveal complex interactions between structural, operational, and financial aspects of their domain. Such systems have further potential in other domains that require collaborative discussion or demonstration of models, forecasts, or cost-benefit analyses to high-level stakeholders.

People new to making and makerspaces often struggle with identifying what tools are available and where they are, understanding how to operate the tools, and predicting how their decisions will affect their final product. From literature on novices and our interviews with expert makers, we identified situation awareness support as one possible way to address some of the challenges faced by novices. We present a set of design goals intended to scaffold situation awareness in a makerspace, and MakeAware, a prototype system we implemented based on those design goals. MakeAware provides a combination of environmental cues, information about the project process, and background knowledge. In a preliminary evaluation, we found MakeAware can help novices make conscious choices during a project and put more emphasis on planning, thereby exhibiting traits associated with having situation awareness while making.

While synchronous one-on-one help for software learning is rich and valuable, it can be difficult to find and connect with someone who can provide assistance. Through a formative user study, we explore the idea of fixed-duration, one-on-one help sessions and find that 3 minutes is often enough time for novice users to explain their problem and receive meaningful help from an expert. To facilitate this type of interaction, we developed MicroMentor, an on-demand help system that connects users via video chat for 3-minute help sessions. MicroMentor automatically attaches relevant supplementary materials and uses contextual information, such as command history and expertise, to encourage the most qualified users to accept incoming requests. These help sessions are recorded and archived, building a bank of knowledge that can further help a broader audience. Through a user study, we find MicroMentor to be useful and successful in connecting users for short teaching moments.

Expressive robots are useful in many contexts, from industrial to entertainment applications. However, designing expressive robot behaviors requires editing a large number of unintuitive control parameters. We present an interactive, data-driven system that allows editing of these complex parameters in a semantic space. Our system combines a physics-based simulation that captures the robot's motion capabilities, and a crowd-powered framework that extracts relationships between the robot's motion parameters and the desired semantic behavior. These relationships enable mixed-initiative exploration of possible robot motions. We specifically demonstrate our system in the context of designing emotionally expressive behaviors. A user-study finds the system to be useful for more quickly developing desirable robot behaviors, compared to manual parameter editing.

The recent proliferation of fabrication and making activities has introduced a large number of users to a variety of tools and equipment. Monitored, reactive and adaptive fabrication spaces are needed to provide personalized information, feedback and assistance to users. This paper explores the sensorization of making and fabrication activities, where the environment, tools, and users were considered to be separate entities that could be instrumented for data collection. From this exploration, we present the design of a modular system that can capture data from the varied sensors and infer contextual information. Using this system, we collected data from fourteen participants with varying levels of expertise as they performed seven representative making tasks. From the collected data, we predict which activities are being performed, which users are performing the activities, and what expertise the users have. We present several use cases of this contextual information for future interactive fabrication spaces.

Remotely instructing and guiding users in physical tasks has offered promise across a wide variety of domains. While it has been the subject of many research projects, current approaches are often limited in the communication bandwidth (lacking context, spatial information) or interactivity (unidirectional, asynchronous) between the expert and the learner. Systems that use Mixed-Reality systems for this purpose have rigid configurations for the expert and the learner. We explore the design space of bi-directional mixed-reality telepresence systems for teaching physical tasks, and present Loki, a novel system which explores the various dimensions of this space. Loki leverages video, audio and spatial capture along with mixed-reality presentation methods to allow users to explore and annotate the local and remote environments, and record and review their own performance as well as their peer's. The system design of Loki also enables easy transitions between different configurations within the explored design space. We validate its utility through a varied set of scenarios and a qualitative user study.

Current haptic devices are often bulky and rigid, making them unsuitable for ubiquitous interaction and scenarios where the user must also interact with the real world. To address this gap, we propose HydroRing, an unobtrusive, finger-worn device that can provide the tactile sensations of pressure, vibration, and temperature on the fingertip, enabling mixed-reality haptic interactions. Different from previous explorations, HydroRing in active mode delivers sensations using liquid travelling through a thin, flexible latex tube worn across the fingerpad, and has minimal impact on a user's dexterity and their perception of stimuli in passive mode. Two studies evaluated participants' ability to perceive and recognize sensations generated by the device, as well as their ability to perceive physical stimuli while wearing the device. We conclude by exploring several applications leveraging this mixed-reality haptics approach.

Dementia is a progressive, degenerative syndrome that erodes cognition, long term memory, and the ability to maintain social relationships. Anxiety is common among those with dementia, and ranges from momentary and mild, to chronic and severe. Listening to familiar music from childhood or early adulthood has been shown to provide therapeutic and positive quality of life effects for individuals with dementia, but most modern interfaces are unfamiliar and difficult to use which may add frustration and stress that music is intended to relieve. To enable individuals with dementia to control playback of music, we present AMI, a tangible music player that can be reconfigured and adapted to meet the changing needs and preferences of individuals. AMI provides a set of input components (e.g., buttons, switches, knobs) with varying physical properties which can be easily interchanged by a non-technical user (such as a caregiver). This work contributes the system design, results of user tests with the target population, as well as a set of design principles that can be used in the development of future interfaces.

Sketching in immersive 3D virtual reality (VR) environments has great potential for a variety of interactive 3D design applications. Precisely sketching the intended strokes in mid-air, however, can be a challenge. In this paper, we present a set of controlled studies to analyze the factors affecting human ability to sketch freely in a 3D VR environment. In our first study, we directly compare traditional sketching on a physical surface to sketching in VR, with and without a physical surface to rest the stylus on. Our results indicate that the lack of a physical drawing surface is a major cause of inaccuracies in VR drawing, and that the effect is dependent on the orientation of the drawing surface. In a second experiment, we evaluate the extent to which visual guidance can compensate for the loss of sketching precision in VR. We found that while additional visual guidance improves positional accuracy, it can be detrimental to the aesthetic quality of strokes. We conclude by distilling our experimental findings into design guidelines for sketching tools in immersive 3D environments.

Interactive, smart objects-customized to individuals and uses-are central to many movements, such as tangibles, the internet of things (IoT), and ubiquitous computing. Yet, rapid prototyping both the form and function of these custom objects can be problematic, particularly for those with limited electronics or programming experience. Designers often need to embed custom circuitry; program its workings; and create a form factor that not only reflects the desired user experience but can also house the required circuitry and electronics. To mitigate this, we created Pineal, a design tool that lets end-users: (1) modify 3D models to include a smart watch or phone as its heart; (2) specify high-level interactive behaviours through visual programming; and (3) have the phone or watch act out such behaviours as the objects' "smarts". Furthermore, a series of prototypes show how Pineal exploits mobile sensing and output, and automatically generates 3D printed form-factors for rich, interactive, objects.

The dramatic decrease in price and increase in availability of hobbyist electronics has led to a wide array of embedded and interactive devices. While electronics have become more widespread, developing and prototyping the required circuitry for these devices is still difficult, requiring knowledge of electronics, components, and programming. In this paper, we present Trigger-Action-Circuits (TAC), an interactive system that leverages generative design to produce circuitry, firmware, and assembly instructions, based on high-level, behavioural descriptions. TAC is able to generate multiple candidate circuits from a behavioural description, giving the user a number of alternative circuits that may be best suited to their use case (e.g., based on cost, component availability or ease of assembly). The generated circuitry uses off-the-shelf, commodity electronics, not specialized hardware components, enabling scalability and extensibility. TAC supports a range of common components and behaviors that are frequently required for prototyping electronic circuits. A user study demonstrated that TAC helps users avoid problems encountered during circuit design and assembly, with users completing their circuits significantly faster than with traditional methods.

Smart rings have a unique form factor suitable for many applications, however, they offer little opportunity to provide the user with natural output. We propose passive kinesthetic force feedback as a novel output method for rotational input on smart rings. With this new output channel, friction force profiles can be designed, programmed, and felt by a user when they rotate the ring. This modality enables new interactions for ring form factors. We demonstrate the potential of this new haptic output method though Frictio, a prototype smart ring. In a controlled experiment, we determined the recognizability of six force profiles, including Hard Stop, Ramp-Up, Ramp-Down, Resistant Force, Bump, and No Force. The results showed that participants could distinguish between the force profiles with 94% accuracy. We conclude by presenting a set of novel interaction techniques that Frictio enables, and discuss insights and directions for future research.

We present RetroFab, an end-to-end design and fabrication environment that allows non-experts to retrofit physical interfaces. Our approach allows for changing the layout and behavior of physical interfaces. Unlike customizing software interfaces, physical interfaces are often challenging to adapt because of their rigidity. With RetroFab, a new physical interface is designed that serves as a proxy interface for the legacy controls that are now operated by actuators. RetroFab makes this concept of retrofitting devices available to non-experts by automatically generating an enclosure structure from an annotated 3D scan. This enclosure structure holds together actuators, sensors as well as components for the redesigned interface. To allow retrofitting a wide variety of legacy devices, the RetroFab design tool comes with a toolkit of 12 components. We demonstrate the versatility and novel opportunities of our approach by retrofitting five domestic objects and exploring their use cases. Preliminary user feedback reports on the experience of retrofitting devices with RetroFab.

In recent years, extensive research in the HCI literature has explored interactive techniques for digital fabrication. However, little attention in this body of work has examined how to involve and guide human workers in fabricating larger-scale structures. We propose a novel model of crowdsourced fabrication, in which a large number of workers and volunteers are guided through the process of building a pre-designed structure. The process is facilitated by an intelligent construction space capable of guiding individual workers and coordinating the overall build process. More specifically, we explore the use of smartwatches, indoor location sensing, and instrumented construction materials to provide real-time guidance to workers, coordinated by a foreman engine that manages the overall build process. We report on a three day deployment of our system to construct a 12-tall bamboo pavilion with assistance from more than one hundred volunteer workers, and reflect on observations and feedback collected during the exhibit.

Mobile devices offer constant connectivity to the world, which can negatively affect in-person interaction. Current approaches to minimizing the social disruption and improving the subtlety of interactions tend to focus on the development of inconspicuous devices that provide basic input or output. This paper presents a more general approach to subtle interaction and demonstrates how a number of principles from magic can be leveraged to improve subtlety. It also presents a framework that can be used to classify subtle interfaces along with a modular set of novel interfaces that fit within this framework. Lastly, the paper presents a new evaluation paradigm specifically designed to assess the subtlety of interactions. This paradigm is used to compare traditional approaches to our new subtle approaches. We find our new approaches are over five times more subtle than traditional interactions, even when participants are aware of the technologies being used.

Scatterplots are an effective and commonly used technique to show the relationship between two variables. However, as the number of data points increases, the chart suffers from "over-plotting" which obscures data points and makes the underlying distribution of the data difficult to discern. Reducing the opacity of the data points is an effective way to address over-plotting, however, setting the individual point opacity is a manual task performed by the chart designer. We present a user-driven model of opacity scaling for scatter plots built from crowd-sourced responses to opacity scaling tasks using several synthetic data distributions, and then test our model on a collection of real-world data sets.

The growth of mobile and wearable technologies has made it often difficult to understand what people in our surroundings are doing with their technology. In this paper, we introduce the concept of candid interaction: techniques for providing awareness about our mobile and wearable device usage to others in the vicinity. We motivate and ground this exploration through a survey on current attitudes toward device usage during interpersonal encounters. We then explore a design space for candid interaction through seven prototypes that leverage a wide range of technological enhancements, such as Augmented Reality, shape memory muscle wire, and wearable projection. Preliminary user feedback of our prototypes highlights the trade-offs between the benefits of sharing device activity and the need to protect user privacy.

Gesture-based interfaces are becoming more prevalent and complex, requiring non-trivial learning of gesture sets. Many methods for learning gestures have been proposed, but they are often evaluated with short-term recall tests that measure user performance, rather than learning. We evaluated four types of gesture guides using a retention and transfer paradigm common in motor learning experiments and found results different from those typically reported with recall tests. The results indicate that many guide systems with higher levels of guidance exhibit high performance benefits while the guide is being used, but are ultimately detrimental to user learning. We propose an adaptive guide that does not suffer from these drawbacks, and that enables a smooth transition from novice to expert. The results contrasting learning and performance can be explained by the guidance hypothesis. They have important implications for the design and evaluation of future gesture learning systems.

YouMove is a novel system that allows users to record and learn physical movement sequences. The recording system is designed to be simple, allowing anyone to create and share training content. The training system uses recorded data to train the user using a large-scale augmented reality mirror. The system trains the user through a series of stages that gradually reduce the user's reliance on guidance and feedback. This paper discusses the design and implementation of YouMove and its interactive mirror. We also present a user study in which YouMove was shown to improve learning and short-term retention by a factor of 2 compared to a traditional video demonstration.

People with a wide spectrum of disabilities, ranging from spinal injuries to autism, have benefited from equine assisted therapy (EAT). Using EAT, therapy patients have improved both physically and psychologically (e.g., demonstrating increased attention, motivation, and communication skills). There are still many open questions regarding this therapy and the reasons for its success. Many of these questions have remained unanswered due in large part to the uncontrolled nature of EAT. The Virtual Equine Assisted Therapy (VEAT) Project integrates a robotic platform with virtual reality technologies to provide a safe, controlled environment through which various aspects of EAT can be isolated and studied. The system incorporates realistic equine motions with visual, auditory, olfactory, and somatosensory stimuli to provide highly immersive experiences to patients.

We explore several ways to estimate movie similarity from the free encyclopedia Wikipedia with the goal of im-proving our predictions for the Netflix Prize. Our system first uses the content and hyperlink structure of Wikipedia articles to identify similarities between movies. We then predict a user’s unknown ratings by using these similarities in conjunction with the user’s known ratings to initialize matrix factorization and k-Nearest Neighbours algorithms. We blend these results with existing ratings-based predic-tors. Finally, we discuss our empirical results, which sug-gest that external Wikipedia data does not significantly im-prove the overall prediction accuracy. 1