Collage is a powerful medium for visual expression, traditionally demanding significant artistic expertise and manual effort. Existing methods often struggle with a trade-off between semantic expression and the visual fidelity of the constituent images. To address this, we introduce SCORE (Semantic Collage by Optimizing Rendered Elements), a novel text-driven framework that automates the creation of semantically rich and structurally sound collages. Our key innovation is to shift the optimization process entirely into the image space. By employing a differentiable renderer, we can backpropagate gradients from a powerful, pre-trained text-to-image model directly to the spatial parameters, including position, rotation, and scale, of each image element. We leverage Variational Score Distillation (VSD) to provide robust semantic guidance from a text prompt, ensuring the final layout aligns with the desired concept. Crucially, our ''minimal editing'' principle preserves the integrity of the original elements by forgoing any content-level modifications. The layout is refined by a joint loss function that combines the VSD-based semantic loss with structural regularizers that penalize overlap and enforce boundary constraints. The output of SCORE is a parametric, structured representation that allows further editing and downstream use. Our work reduces the barrier to creative expression and provides a new, powerful paradigm for organizing visual contents.

In the field of sketch generation, raster-format trained models often produce non-stroke artifacts, while vector-format trained models typically lack a holistic understanding of sketches, leading to compromised recognizability. Moreover, existing methods struggle to extract common features from similar elements (e.g., eyes of animals) appearing at varying positions across sketches. To address these challenges, we propose StrokeFusion, a two-stage framework for vector sketch generation. It contains a dual-modal sketch feature learning network that maps strokes into a high-quality latent space. This network decomposes sketches into normalized strokes and jointly encodes stroke sequences with Unsigned Distance Function (UDF) maps, representing sketches as sets of stroke feature vectors. Building upon this representation, our framework exploits a stroke-level latent diffusion model that simultaneously adjusts stroke position, scale, and trajectory during generation. This enables high-fidelity stroke generation while supporting stroke interpolation editing. Extensive experiments across multiple sketch datasets, demonstrate that our framework outperforms state-of-the-art techniques, validating its effectiveness in preserving structural integrity and semantic features.

Existing automatic graphical user interface (GUI) generation frameworks primarily focus on either GUI prototype generation or GUI code conversion, failing to achieve end-to-end functional GUI generation. This paper proposes a framework named AutoStructGUI that bridges the gap between layout prototype design and functional code implementation in the development of a GUI. The framework adopts trees as the representation of GUI layouts, exploits a Transformer architecture, and uses two schemes for the serialization of GUI layout trees, enabling both global and local generation of structured GUI layouts. This framework was developed as an Android Studio plug-in to facilitate GUI layout design and functional code generation. A user study was conducted to investigate the proposed framework against the conventional GUI creation procedure. The results confirmed that the proposed framework can significantly improve development efficiency, enhance user experience, and increase GUI layout quality. The code of the framework will be released upon the acceptance of the paper.

With the rising ubiquity of digital touch devices and sketch-based interfaces, freehand sketching has become an essential mode of visual communication. Nevertheless, interpreting these often ambiguous and sparse sketches poses challenges for computers. This paper presents Sketchformer++, a hierarchical transformer architecture for the neural representation of vector sketches. It treats a vector sketch as a three-level structure, at sketch level, stroke level, and segment level. Three self-attention modules are adopted in the network architecture, corresponding to the sketch hierarchy. The semantics of sketches are aggregated from local to global levels, resulting in neural representations of sketches. Extensive experiments show that Sketchformer++ helps to achieve superior performance in various downstream tasks, including sketch reconstruction, sketch recog-nition, sketch semantic segmentation, and sketch retrieval, demonstrating its robustness and effectiveness as a means of sketch representation. Code is available at https://github.com/BHR7/SketchformerPlus.

Structured grid layouts are preferable in many 2D visual content creation scenarios since their structures facilitate further layout editing. Multiple geometry-based methods can effectively create structured grid layouts but require user-provided constraints or rules. Existing data-driven approaches have achieved remarkable layout generation performance, but fail to produce appropriate layout structures. We present GTLayout, a novel generative model for structured grid layout generation. We adopt general trees to represent structured grid layouts and exploit a recursive neural network (RvNN) for this generation task. Our model can handle grid layouts with varied structures and regular arrangements. Qualitative and quantitative experiments on public grid layout datasets show that our method outperforms several baselines in the tasks of layout reconstruction and layout generation, especially for datasets containing few samples. We also demonstrate that the structured layout space constructed by our method can blend structures of layouts, as well as providing a visualization and analysis of the layout space. Additionally, we consider two application cases based on GTLayout : multiple layout interpolation and conditional layout generation. Our code is available at https://github.com/Warren-swr/GT-Layout.

Found object drawing is a creative art form incorporating everyday objects into imaginative images, offering a refreshing and unique way to express ideas. However, for many people, creating this type of work can be challenging due to difficulties in generating creative ideas and finding suitable reference images to help translate their ideas onto paper. Based on the findings of a formative study, we propose GenFODrawing, a creativity support tool to help users create diverse found object drawings. Our system provides AI-driven textual and visual inspirations, and enhances controllability through sketch-based and box-conditioned image generation, enabling users to create personalized outputs. We conducted a user study with twelve participants to compare GenFODrawing, to a baseline condition where the participants completed the creative tasks using their own desired approaches without access to our system. The study demonstrated that GenFODrawing, enabled easier exploration of diverse ideas, greater agency and control through the creative process, and higher creativity support compared to the baseline. A further open-ended study demonstrated the system's usability and expressiveness, and all participants found the creative process engaging.

Creating interactive scenes often involves complex programming tasks. Although large language models (LLMs) like ChatGPT can generate code from natural language, their output is often error-prone, particularly when scripting interactions among multiple elements. The linear conversational structure limits the editing of individual elements, and the lack of graphical and precise control complicates visual integration. To address these issues, we integrate a context-aware modularization technique that processes textual descriptions for individual elements through separate LLM modules, with a central module managing interactions among elements. It defines a top-down structure to manage interactions, ensuring clear update logic and facilitating efficient collaboration while allowing for independent updates for each element. We design a graphical user interface, MoGraphGPT, which combines modular LLMs with enhanced graphical control to generate codes for 2D interactive scenes. It enables direct integration of graphical information and offers quick, precise control through automatically generated sliders. A comparative study with Cursor Composer shows MoGraphGPT significantly improves easiness, controllability, and performance in creating 2D interactive scenes with multiple visual elements in a coding-free manner. An ablation study validates the effectiveness of modularization, and an open-ended study demonstrates the usability and expressiveness of MoGraphGPT.

People often need guidance to complete tasks with specific requirements or sophisticated steps, such as preparing a meal or assembling furniture. Traditional guidance often relies on unstructured paper instructions that require people to switch between reading instructions and performing actions, resulting in an unsmooth user experience. Recent Mixed Reality (MR) systems alleviate this problem by giving spatialized navigation but demand an authoring step and, therefore, cannot be easily adapted to general tasks. We propose MRPilot, an MR system empowered by Large Language Models (LLMs) and Computer Vision techniques, offering responsive navigation for general tasks without pre-authoring. MRPilot consists of three modules: a Navigation Builder Module using LLMs to generate structured instructions, an Object Anchor Module exploiting Computer Vision techniques to anchor physical objects with virtual proxies, and an Action Recommendation Module giving responsive navigation according to users’ interactions with physical objects. MRPilot bridges the gap between virtual instructions and physical interactions for general tasks, providing contextual and responsive navigation. We conducted a user study to compare MRPilot with a baseline MR system that also exploited LLMs. The results confirmed the effectiveness of MRPilot.

Structured layouts are preferable in many 2D visual contents (e.g., GUIs, webpages) since the structural information allows convenient layout editing. Computational frameworks can help create structured layouts but require heavy labor input. Existing data-driven approaches are effective in automatically generating fixed layouts but fail to produce layout structures. We present StructLayoutFormer, a novel Transformer-based approach for conditional structured layout generation. We use a structure serialization scheme to represent structured layouts as sequences. To better control the structures of generated layouts, we disentangle the structural information from the element placements. Our approach is the first data-driven approach that achieves conditional structured layout generation and produces realistic layout structures explicitly. We compare our approach with existing data-driven layout generation approaches by including post-processing for structure extraction. Extensive experiments have shown that our approach exceeds these baselines in conditional structured layout generation. We also demonstrate that our approach is effective in extracting and transferring layout structures.

Color smudge operations from digital painting software enable users to create natural shading effects in high-fidelity paintings by interactively mixing colors. To precisely control results in traditional painting software, users tend to organize flat-filled color regions in multiple layers and smudge them to generate different color gradients. However, the requirement to carefully deal with regions makes the smudging process time-consuming and laborious, especially for non-professional users. This motivates us to investigate how to infer user-desired smudging effects when users smudge over regions in a single layer. To investigate improving color smudge performance, we first conduct a formative study. Following the findings of this study, we design SmartSmudge, a novel smudge tool that offers users dynamical smudge brushes and real-time region selection for easily generating natural and efficient shading effects. We demonstrate the efficiency and effectiveness of the proposed tool via a user study and quantitative analysis.

AR applications commonly contain diverse interactions among different AR contents. Creating such applications requires creators to have advanced programming skills for scripting interactive behaviors of AR contents, repeated transferring and adjustment of virtual contents from virtual to physical scenes, testing by traversing between desktop interfaces and target AR scenes, and digitalizing AR contents. Existing immersive tools for prototyping/authoring such interactions are tailored for domain-specific applications. To support programming general interactive behaviors of real object(s)/environment(s) and virtual object(s)/environment(s) for novice AR creators, we propose ProInterAR, an integrated visual programming platform to create immersive AR applications with a tablet and an AR-HMD. Users can construct interaction scenes by creating virtual contents and augmenting real contents from the view of an AR-HMD, script interactive behaviors by stacking blocks from a tablet UI, and then execute and control the interactions in the AR scene. We showcase a wide range of AR application scenarios enabled by ProInterAR, including AR game, AR teaching, sequential animation, AR information visualization, etc. Two usability studies validate that novice AR creators can easily program various desired AR applications using ProInterAR.

Abstract We present GLTScene, a novel data‐driven method for high‐quality furniture layout synthesis with general room boundaries as conditions. This task is challenging since the existing indoor scene datasets do not cover the variety of general room boundaries. We incorporate the interior design principles with learning techniques and adopt a global‐to‐local strategy for this task. Globally, we learn the placement of furniture objects from the datasets without considering their alignment. Locally, we learn the alignment of furniture objects relative to their nearest walls, according to the alignment principle in interior design. The global placement and local alignment of furniture objects are achieved by two transformers respectively. We compare our method with several baselines in the task of furniture layout synthesis with general room boundaries as conditions. Our method outperforms these baselines both quantitatively and qualitatively. We also demonstrate that our method can achieve other conditional layout synthesis tasks, including object‐level conditional generation and attribute‐level conditional generation. The code is publicly available at https://github.com/WWalter-Lee/GLTScene .

We present a novel method for blending hierarchical layouts with semantic labels. The core of our method is a hierarchical structure correspondence algorithm, which recursively finds optimal substructure correspondences, achieving a globally optimal correspondence between a pair of hierarchical layouts. This correspondence is consistent with the structures of both layouts, allowing us to define the union of the layouts' structures. The resulting compound structure helps extract intermediate layout structures, from which blended layouts can be generated via an optimization approach. The correspondence also defines a similarity measure between layouts in a hierarchically structured view. Our method provides a new way for novel layout creation. The introduced structural similarity measure regularizes the layouts in a hyperspace. We demonstrate two applications in this paper, i.e., exploratory design of novel layouts and sketch-based layout retrieval, and test them on a magazine layout dataset. The effectiveness and feasibility of these two applications are confirmed by the user feedback and the extensive results. The code is available at https://github.com/lyf7115/LayoutBlending.

We present WireRoom , a computational framework for the intelligent design of abstract 3D wire art to depict a given 3D model. Our algorithm generates a set of 3D wire shapes from the 3D model with informative, visually pleasing, and concise structures. It is achieved by solving a dynamic travelling salesman problem on the surface of the 3D model with a multi-path expansion approach. We introduce a novel explorative computational design procedure by taking the generated wire shapes as candidates, avoiding manual design of the wire shape structure. We compare our algorithm with a baseline method and conduct a user study to investigate the usability of the framework and the quality of the produced wire shapes. The results of the comparison and user study confirm that our framework is effective for producing informative, visually pleasing, and concise wire shapes.

Specifying precise relationships among graphic elements is often a time-consuming process with traditional alignment tools. Automatic beautification of roughly designed layouts can provide a more efficient solution but often lead to undesired results due to ambiguity problems. To facilitate ambiguity resolution in layout beautification, we present a novel user interface for visualizing and editing inferred relationships through an automatic global layout beautification process. First, our interface provides a preview of the beautified layout with inferred constraints without directly modifying an input layout. In this way, the user can easily keep refining beautification results by interactively repositioning and/or resizing elements in the input layout. Second, we present a gestural interface for editing automatically inferred constraints by directly interacting with the visualized constraints via simple gestures. Our technique is applicable to both 2D and 3D global layout beautification, supported by efficient system implementation that provides instant user feedback. Our user study validates that our tool is capable of creating, editing, and refining layouts of graphic elements, and is significantly faster than the standard snap-dragging or command-based alignment tools for both 2D and 3D layout tasks.

We present a novel method to produce discernible image mosaics, with relatively large image tiles replaced by images drawn from a database, to resemble a target image. Compared to existing works on image mosaics, the novelty of our method is two-fold. Firstly, believing that the presence of visual edges in the final image mosaic strongly supports image perception, we develop an edge-aware photo retrieval scheme which emphasizes the preservation of visual edges in the target image. Secondly, unlike most previous works which apply a pre-determined partition to an input image, our image mosaics are composed of adaptive tiles, whose sizes are determined based on the available images in the database and the objective of maximizing resemblance to the target image. We show discernible image mosaics obtained by our method, using image collections of only moderate size. To evaluate our method, we conducted a user study to validate that the image mosaics generated present both globally and locally appropriate visual impressions to the human observers. Visual comparisons with existing techniques demonstrate the superiority of our method in terms of mosaic quality and perceptibility.

We present a novel 3D model-guided interface for in-situ sketching on 3D planes. Our work is motivated by evolutionary design, where existing 3D objects form the basis for conceptual re-design or further design exploration. We contribute a novel workflow that exploits the geometry of an underlying 3D model to infer 3D planes on which 2D strokes drawn that are on and around the 3D model should be meaningfully projected. This provides users with the nearly modeless fluidity of a sketching interface, and is particularly useful for 3D sketching over planes that are not easily accessible or do not preexist. We also provide an additional set of tools, including sketching with explicit plane selection and model-aware canvas manipulation. Our system is evaluated with a user study, showing that our technique is easy to learn and effective for rapid sketching of product design variations around existing 3D models.

The creation of 3D contents still remains one of the most crucial problems for the emerging applications such as 3D printing and Augmented Reality. In Augmented Reality, how to create virtual contents that seamlessly overlay with the real environment is a key problem for human-computer interaction and many subsequent applications. In this paper, we present a sketch-based interactive tool, which we term emph{SweepCanvas}, for rapid exploratory 3D modeling on top of an RGB-D image. Our aim is to offer end-users a simple yet efficient way to quickly create 3D models on an image. We develop a novel sketch-based modeling interface, which takes a pair of user strokes as input and instantly generates a curved 3D surface by sweeping one stroke along the other. A key enabler of our system is an optimization procedure that extracts pairs of spatial planes from the context to position and sweep the strokes. We demonstrate the effectiveness and power of our modeling system on various RGB-D data sets and validate the use cases via a pilot study.

In this work we introduce 2D-Dragger, a unified touch-based target acquisition technique that enables easy access to small targets in dense regions or distant targets on screens of various sizes. The effective width of a target is constant with our tool, allowing a fixed scale of finger movement for capturing a new target. Our tool is thus insensitive to the distribution and size of the selectable targets, and consistently works well for screens of different sizes, from mobile to wall-sized screens. Our user studies show that overall 2D-Dragger performs the best compared to the state-of-the-art techniques for selecting both near and distant targets of various sizes in different densities.

Many graphic applications rely on command-based arrangement tools to achieve precise layouts. Traditional tools are designed to operate on a single group of elements that are distributed consistently with the arrangement axis implied by a command. This often demands a process with repeated element selections and arrangement commands to achieve 2D layouts involving multiple rows and/or columns of well aligned and/or distributed elements. Our work aims to reduce the numbers of selection operation and command invocation, since such reductions are particularly beneficial to professional designers who design lots of layouts. Our key idea is that an issued arrangement command is in fact very informative, instructing how to automatically decompose a 2D layout into multiple 1D groups, each of which is compatible with the command. We present a parameter-free, command-driven grouping approach so that users can easily predict our grouping results. We also design a simple user interface with pushpins to enable explicit control of grouping and arrangement. Our user study confirms the intuitiveness of our technique and its performance improvement over traditional command-based arrangement tools.

Automatic global beautification methods have been proposed for sketch-based interfaces, but they can lead to undesired results due to ambiguity in the user's input. To facilitate ambiguity resolution in layout beautification, we present a novel user interface for visualizing and editing inferred relationships. First, our interface provides a preview of the beautified layout with inferred constraints, without directly modifying the input layout. In this way, the user can easily keep refining beautification results by interactively repositioning and/or resizing elements in the input layout. Second, we present a gestural interface for editing automatically inferred constraints by directly interacting with the visualized constraints via simple gestures. Our efficient implementation of the beautification system provides the user instant feedback. Our user studies validate that our tool is capable of creating, editing and refining layouts of graphic elements and is significantly faster than the standard snap-dragging and command-based alignment tools.

This paper introduces a simple yet effective shape analysis mechanism for geometry processing. Unlike traditional shape analysis techniques which compute descriptors per surface point up to certain neighborhoods, we introduce a shape analysis framework in which the descriptors are based on pairs of surface points. Such a pairwise analysis approach leads to a new class of shape descriptors that are more global, discriminative, and can effectively capture the variations in the underlying geometry. Specifically, we introduce new shape descriptors based on the isocurves of harmonic functions whose global maximum and minimum occur at the point pair. We show that these shape descriptors can infer shape structures and consistently lead to simpler and more efficient algorithms than the state-of-the-art methods for three applications: intrinsic reflectional symmetry axis computation, matching shape extremities, and simultaneous surface segmentation and skeletonization.

This paper presents Lazy Selection , a scribble-based tool for quick selection of one or more desired shape elements by roughly stroking through the elements. Our algorithm automatically refines the selection and reveals the user's intention. To give the user maximum flexibility but least ambiguity, our technique first extracts selection candidates from the scribble-covered elements by examining the underlying patterns and then ranks them based on their location and shape with respect to the user-sketched scribble. Such a design makes our tool tolerant to imprecise input systems and applicable to touch systems without suffering from the fat finger problem. A preliminary evaluation shows that compared to the standard click and lasso selection tools, which are the most commonly used, our technique provides significant improvements in efficiency and flexibility for many selection scenarios.

This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information. The method locates concave creases and seams using a set of concavity-sensitive scalar fields. These fields are computed by solving a Laplacian system with a novel concavity-sensitive weighting scheme. Isolines sampled from the concavity-aware fields naturally gather at concave seams, serving as good cutting boundary candidates. In addition, the fields provide sufficient information allowing efficient evaluation of the candidate cuts. We perform a summarization of all field gradient magnitudes to define a score for each isoline and employ a score-based greedy algorithm to select the best cuts. Extensive experiments and quantitative analysis have shown that the quality of our segmentations are better than or comparable with existing state-of-the-art more complex approaches.