Computer Graphics Forum最新文献

Despite the remarkable progress in 3D talking head generation, directly generating 3D talking human avatars still suffers from rigid facial expressions, distorted hand textures and out-of-sync lip movements. In this paper, we extend speaker-specific talking head generation task to talking human avatar synthesis and propose a novel pipeline, THGS, that animates lifelike Talking Human avatars using 3D Gaussian Splatting (3DGS). Given speech audio, expression and body poses as input, THGS effectively overcomes the limitations of 3DGS human re-construction methods in capturing expressive dynamics, such as mouth movements, facial expressions and hand gestures, from a short monocular video. Firstly, we introduce a simple yet effective Learnable Expression Blendshapes (LEB) for facial dynamics re-construction, where subtle facial dynamics can be generated by linearly combining the static head model and expression blendshapes. Secondly, a Spatial Audio Attention Module (SAAM) is proposed for lip-synced mouth movement animation, building connections between speech audio and mouth Gaussian movements. Thirdly, we employ a body pose, expression and skinning weights joint optimization strategy to optimize these parameters on the fly, which aligns hand movements and expressions better with video input. Experimental results demonstrate that THGS can achieve high-fidelity 3D talking human avatar animation at 150+ fps on a web-based rendering system, improving the requirements of real-time applications. Our project page is at https://sora158.github.io/THGS.github.io/.

Oceanographers rely on visual analysis to interpret model simulations, identify events and phenomena, and track dynamic ocean processes. The ever increasing resolution and complexity of ocean data due to its dynamic nature and multivariate relationships demands a scalable and adaptable visualization tool for interactive exploration. We introduce pyParaOcean, a scalable and interactive visualization system designed specifically for ocean data analysis. pyParaOcean offers specialized modules for common oceanographic analysis tasks, including eddy identification and salinity movement tracking. These modules seamlessly integrate with ParaView as filters, ensuring a user-friendly and easy-to-use system while leveraging the parallelization capabilities of ParaView and a plethora of inbuilt general-purpose visualization functionalities. The creation of an auxiliary dataset stored as a Cinema database helps address I/O and network bandwidth bottlenecks while supporting the generation of quick overview visualizations. We present a case study on the Bay of Bengal to demonstrate the utility of the system and scaling studies to evaluate the efficiency of the system.

Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field.

Khanteimouri P., Mandad M., Campen M.: Rational Bézier Guarding. Computer Graphics Forum 41, 5 (2022), 89–99.

The proof of Lemma 5 contains a mistake; while various kinds of variation diminishing properties of Bézier triangles are known, one of the specific geometric kind assumed is not available. The lemma does not hold in full generality as stated, for arbitrary Bézier triangles. Whether it holds for the restricted class of Bézier triangles constructed by the method is an open question, no counterexample is known.

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The hybrid Lagrangian/Eulerian formulation of continuum shells is highly effective for producing challenging simulations of thin materials like cloth with bending resistance and frictional contact. However, existing formulations are restricted to materials that do not undergo tearing nor fracture due to the difficulties associated with incorporating strong discontinuities of field quantities like velocity via basis enrichment while maintaining $$ continuity or $$ regularity. We propose an extension of this formulation to simulate dynamic tearing and fracturing of thin shells using Kirchhoff–Love continuum theory. Damage, which manifests as cracks or tears, is propagated by tracking the evolution of a time-dependent phase-field in the co-dimensional manifold, where a moving least-squares (MLS) approximation then captures the strong discontinuities of interpolated field quantities near the crack. Our approach is capable of simulating challenging scenarios of this tearing and fracture, all-the-while harnessing the existing benefits of the hybrid Lagrangian/Eulerian formulation to expand the domain of possible effects. The method is also amenable to user-guided control, which serves to influence the propagation of cracks or tears such that they follow prescribed paths during simulation.

Cluster separation is a task typically tackled by widely used clustering techniques, such as k-means or DBSCAN. However, these algorithms are based on non-perceptual metrics, and our experiments demonstrate that their output does not reflect human cluster perception. To bridge the gap between human cluster perception and machine-computed clusters, we propose HPSCAN, a learning strategy that operates directly on scattered data. To learn perceptual cluster separation on such data, we crowdsourced the labeling of $$ bivariate (scatterplot) datasets to 384 human participants. We train our HPSCAN model on these human-annotated data. Instead of rendering these data as scatterplot images, we used their x and y point coordinates as input to a modified PointNet++ architecture, enabling direct inference on point clouds. In this work, we provide details on how we collected our dataset, report statistics of the resulting annotations, and investigate the perceptual agreement of cluster separation for real-world data. We also report the training and evaluation protocol for HPSCAN and introduce a novel metric, that measures the accuracy between a clustering technique and a group of human annotators. We explore predicting point-wise human agreement to detect ambiguities. Finally, we compare our approach to 10 established clustering techniques and demonstrate that HPSCAN is capable of generalizing to unseen and out-of-scope data.

Novel view synthesis for talking heads presents significant challenges due to the complex and diverse motion transformations involved. Conventional methods often resort to reliance on structure priors, like facial templates, to warp observed images into a canonical space conducive to rendering. However, the incorporation of such priors introduces a trade-off-while aiding in synthesis, they concurrently amplify model complexity, limiting generalizability to other deformable scenes. Departing from this paradigm, we introduce a pioneering solution: the motion-conditioned neural radiance field, MoNeRF, designed to model talking heads through latent motion navigation. At the core of MoNeRF lies a novel approach utilizing a compact set of latent codes to represent orthogonal motion directions. This innovative strategy empowers MoNeRF to efficiently capture and depict intricate scene motion by linearly combining these latent codes. In an extended capability, MoNeRF facilitates motion control through latent code adjustments, supports view transfer based on reference videos, and seamlessly extends its applicability to model human bodies without necessitating structural modifications. Rigorous quantitative and qualitative experiments unequivocally demonstrate MoNeRF's superior performance compared to state-of-the-art methods in talking head synthesis. We will release the source code upon publication.

Research shows that user traits can modulate the use of visualization systems and have a measurable influence on users' accuracy, speed, and attention when performing visual analysis. This highlights the importance of user-adaptive visualization that can modify themselves to the characteristics and preferences of the user. However, there are very few such visualization systems, as creating them requires broad knowledge from various sub-domains of the visualization community. A user-adaptive system must consider which user traits they adapt to, their adaptation logic and the types of interventions they support. In this STAR, we survey a broad space of existing literature and consolidate them to structure the process of creating user-adaptive visualizations into five components: Capture Ⓐ Input from the user and any relevant peripheral information. Perform computational Ⓑ User Modelling with this input to construct a Ⓒ User Representation. Employ Ⓓ Adaptation Assignment logic to identify when and how to introduce Ⓔ Interventions. Our novel taxonomy provides a road map for work in this area, describing the rich space of current approaches and highlighting open areas for future work.

User confidence plays an important role in guided visual data analysis scenarios, especially when uncertainty is involved in the analytical process. However, measuring confidence in practical scenarios remains an open challenge, as previous work relies primarily on self-reporting methods. In this work, we propose a quantitative approach to measure user confidence—as opposed to trust—in an analytical scenario. We do so by exploiting the respective user interaction provenance graph and examining the impact of guidance using a set of network metrics. We assess the usefulness of our proposed metrics through a user study that correlates results obtained from self-reported confidence assessments and our metrics—both with and without guidance. The results suggest that our metrics improve the evaluation of user confidence compared to available approaches. In particular, we found a correlation between self-reported confidence and some of the proposed provenance network metrics. The quantitative results, though, do not show a statistically significant impact of the guidance on user confidence. An additional descriptive analysis suggests that guidance could impact users' confidence and that the qualitative analysis of the provenance network topology can provide a comprehensive view of changes in user confidence. Our results indicate that our proposed metrics and the provenance network graph representation support the evaluation of user confidence and, subsequently, the effective development of guidance in VA.