IEEE transactions on visualization and computer graphics最新文献

Monocular dynamic video reconstruction is a typical ill-posed problem due to the limited observations and complex 3D motions. Despite the recent advances in dynamic 3D Gaussian splatting techniques, most of them still struggle with the monocular setting, since they heavily rely on geometric cues from multiple cameras or ignore the structural coherence among the optimized 3D Gaussains. To address this, we propose Hie4DGS, a novel hierarchical structure representation to model the complex dynamic motions from monocular dynamic videos. Specifically, we decompose the motions of a dynamic scene into groups of multiple structure granularities and progressively compose them to derive the motion of each 3D Gaussian. Building on this representation, we leverage hierarchical semantic segmentation to group Gaussians and initialize their motion using depth and tracking priors within each group. Additionally, we introduce a structure rendering loss that enforces consistency between the learned motion structure and semantic priors, further reducing motion ambiguity. Compared to the state-of-the-art dynamic Gaussian methods, we achieve significant improvement in rendering quality on monocular video datasets featuring complex real-world motions.

With the advancement of haptic interfaces, recent studies have focused on enabling detailed haptic experiences in virtual reality (VR), such as fluid-haptic interaction. However, rendering forces from fluid contact often causes a high-cost computation. Given that motion-induced fluid feedback is crucial to the overall experience, we focus on hand-perceivable forces to enhance underwater haptic sensation by achieving high-fidelity rendering while considering human perceptual capabilities. We present a new multimodal (tactile and kinesthetic) haptic rendering pipeline. Here, we employ drag and added mass forces by dynamically adapting to the user's hand movement and posture with pneumatic-based haptic gloves. We defined decaying and damping effects to indicate fluid properties caused by inertia and confirmed their significant perceptual impacts compared to using only physics-based equations in a perception study. By modulating pressure variations, we reproduced fluid smoothness via exponential tactile deflation and light fluid mass via linear kinesthetic feedback. Our pipeline enabled richer and more immersive VR underwater experiences by accounting for precise hand regions and motion diversity.

Despite the remarkable process of talking-head-based avatar-creating solutions, directly generating anchor-style videos with full-body motions remains challenging. In this study, we propose Make-Your-Anchor+, a novel system necessitating only a one-minute video clip of an individual for training, subsequently enabling the automatic generation of anchor-style videos with precise torso and hand movements. Specifically, we finetune a proposed structure-guided diffusion model on input video to render 3D mesh conditions into human appearances. We adopt a two-stage training strategy for the diffusion model, effectively mapping movements with specific appearances to create digital avatars for online streamers, live shopping hosts, and other applications. To produce arbitrary long temporal video, we extract human motion information from video diffusion prior by adapting the frame-wise diffusion model to pretrained video diffusion weights with lower cost, and a simple yet effective batch-overlapped temporal denoising module is proposed to bypass the constraints on video length during inference. Finally, a novel identity-specific face enhancement module is introduced to improve the visual quality of facial regions in the output videos. Comparative experiments demonstrate the system's effectiveness and superiority in visual quality, temporal coherence, and identity preservation, outperforming SOTA diffusion/non-diffusion methods.

Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden. Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization researchers at all stages of designing and conducting browser-based user studies. ReVISit supports researchers in the design, debug & pilot, data collection, analysis, and dissemination experiment phases by providing both technical affordances (such as replay of participant interactions) and sociotechnical aids (such as a mindfully maintained community of support). It is a proven system that can be (and has been) used in publication-quality studies-which we demonstrate through a series of experimental replications. We reflect on the design of the system via interviews and an analysis of its technical dimensions. Through this work, we seek to elevate the ease with which studies are conducted, improve the reproducibility of studies within our community, and support the construction of advanced interactive studies.

In this paper, we study the propagation of data uncertainty through the marching cubes algorithm for isosurface visualization for correlated uncertain data. Consideration of correlation has been shown paramount for avoiding errors in uncertainty quantification and visualization in multiple prior studies. Although the problem of isosurface uncertainty with spatial data correlation has been previously addressed, there are two major limitations to prior treatments. First, there are no analytical formulations for uncertainty quantification of isosurfaces when the data uncertainty is characterized by a Gaussian distribution with spatial correlation. Second, as a consequence of the lack of analytical formulations,existing techniques resort to a Monte Carlo sampling approach, which is expensive and difficult to integrate into visualization tools. To address these limitations, we present a closed-form framework to efficiently derive uncertainty in marching cubes level-sets for Gaussian uncertain data with spatial correlation (MAGIC). To derive closed-form solutions, we leverage the Hinkley's derivation on the ratio of Gaussian distributions. With our analytical framework, we achieve a significant speed-up and enhanced accuracy of uncertainty quantification over classical Monte Carlo methods. We further accelerate our analytical solutions using many-core processors to achieve speed-ups up to $585 times$ and integrability with production visualization tools for broader impact. We demonstrate the effectiveness of our correlation-aware uncertainty framework through experiments on meteorology, urban flow, and astrophysics simulation datasets.

Creating detailed 3D characters from a single image remains challenging due to the difficulty in separating semantic components during generation. Existing methods often produce entangled meshes with poor topology, hindering downstream applications like rigging and animation. We introduce SeparateGen, a novel framework that generates high-quality 3D characters by explicitly reconstructing them as distinct semantic components (e.g., body, clothing, hair, shoes) from a single, arbitrary-pose image. SeparateGen first leverages a multi-view diffusion model to generate consistent multi-view images in a canonical Apose. Then, a novel component-aware reconstruction model, SC-LRM, conditioned on these multi-view images, adaptively decomposes and reconstructs each component with high fidelity. To train and evaluate SeparateGen, we contribute SC-Anime, the first large-scale dataset of 7,580 anime-style 3D characters with detailed component-level annotations. Extensive experiments demonstrate that SeparateGen significantly outperforms stateof- the-art methods in both reconstruction quality and multiview consistency. Furthermore, our component-based approach effectively resolves mesh entanglement issues, enabling seamless rigging and asset reuse. SeparateGen thus represents a step towards generating high-quality, application-ready 3D characters from a single image. The SC-Anime dataset and our code will be publicly released.

Detecting and interpreting common patterns in relational data is crucial for understanding complex topological structures across various domains. These patterns, or network motifs, can often be detected algorithmically. However, visual inspection remains vital for exploring and discovering patterns. This paper focuses on presenting motifs within BioFabric network visualizations-a unique technique that opens opportunities for research on scaling to larger networks, design variations, and layout algorithms to better expose motifs. Our goal is to show how highlighting motifs can assist users in identifying and interpreting patterns in BioFabric visualizations. To this end, we leverage existing motif simplification techniques. We replace edges with glyphs representing fundamental motifs such as staircases, cliques, paths, and connector nodes. The results of our controlled experiment and usage scenarios demonstrate that motif simplification for BioFabric is useful for detecting and interpreting network patterns. Our participants were faster and more confident using the simplified view without sacrificing accuracy. The efficacy of our current motif simplification approach depends on which extant layout algorithm is used. We hope our promising findings on user performance will motivate future research on layout algorithms tailored to maximizing motif presentation. Our supplemental material is available at https://osf.io/f8s3g/?view_only=7e2df9109dfd4e6c85b89ed828320843.

Understanding the behavior of large language models (LLMs) is crucial for ensuring their safe and reliable use. However, existing explainable AI (XAI) methods for LLMs primarily rely on word-level explanations, which are often computationally inefficient and misaligned with human reasoning processes. Moreover, these methods often treat explanation as a one-time output, overlooking its inherently interactive and iterative nature. In this paper, we present LLM Analyzer, an interactive visualization system that addresses these limitations by enabling intuitive and efficient exploration of LLM behaviors through counterfactual analysis. Our system features a novel algorithm that generates fluent and semantically meaningful counterfactuals via targeted removal and replacement operations at user-defined levels of granularity. These counterfactuals are used to compute feature attribution scores, which are then integrated with concrete examples in a table-based visualization, supporting dynamic analysis of model behavior. A user study with LLM practitioners and interviews with experts demonstrate the system's usability and effectiveness, emphasizing the importance of involving humans in the explanation process as active participants rather than passive recipients.