Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242320.jpg

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Expressive Whole-Body 3D Gaussian Avatar,noticeable artifacts under novel motions. To address them, we introduce our hybrid representation of the mesh and 3D Gaussians. Our hybrid representation treats each 3D Gaussian as a vertex on the surface with pre-defined connectivity information (., triangle faces) between them following the mesh t

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Controllable Human-Object Interaction Synthesis, contact. To overcome these problems, we introduce an object geometry loss as additional supervision to improve the matching between generated object motion and input object waypoints; we also design guidance terms to enforce contact constraints during the sampling process of the trained diffusion m

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,PAV: Personalized Head Avatar from?Unstructured Video Collection,NeRF framework to model appearance and shape variations in a single unified network for multi-appearances of the same subject. We demonstrate experimentally that PAV outperforms the baseline method in terms of visual rendering quality in our quantitative and qualitative studies on various subjects.

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,Strike a?Balance in?Continual Panoptic Segmentation,nnotated only for the classes of their original step, we devise balanced anti-misguidance losses, which combat the impact of incomplete annotations without incurring classification bias. Building upon these innovations, we present a new method named Balanced Continual Panoptic Segmentation (BalConpa

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dandruff

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,UniTalker: Scaling up?Audio-Driven 3D Facial Animation Through A Unified Model,, typically less than 1?h, to 18.5?h. With a single trained UniTalker model, we achieve substantial lip vertex error reductions of 9.2% for BIWI dataset and 13.7% for Vocaset. Additionally, the pre-trained UniTalker exhibits promise as the foundation model for audio-driven facial animation tasks. Fi

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