RealityAvatar: Towards Realistic Loose Clothing Modeling in Animatable 3D Gaussian Avatars
- URL: http://arxiv.org/abs/2504.01559v1
- Date: Wed, 02 Apr 2025 09:59:12 GMT
- Title: RealityAvatar: Towards Realistic Loose Clothing Modeling in Animatable 3D Gaussian Avatars
- Authors: Yahui Li, Zhi Zeng, Liming Pang, Guixuan Zhang, Shuwu Zhang,
- Abstract summary: We propose RealityAvatar, an efficient framework for high-fidelity digital human modeling, specifically targeting loosely dressed avatars.<n>By incorporating a motion trend module and a latentbone encoder, we explicitly model pose-dependent deformations and temporal variations in clothing behavior.<n>Our method significantly enhances structural fidelity and perceptual quality in dynamic human reconstruction, particularly in non-rigid regions.
- Score: 4.332718737928592
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modeling animatable human avatars from monocular or multi-view videos has been widely studied, with recent approaches leveraging neural radiance fields (NeRFs) or 3D Gaussian Splatting (3DGS) achieving impressive results in novel-view and novel-pose synthesis. However, existing methods often struggle to accurately capture the dynamics of loose clothing, as they primarily rely on global pose conditioning or static per-frame representations, leading to oversmoothing and temporal inconsistencies in non-rigid regions. To address this, We propose RealityAvatar, an efficient framework for high-fidelity digital human modeling, specifically targeting loosely dressed avatars. Our method leverages 3D Gaussian Splatting to capture complex clothing deformations and motion dynamics while ensuring geometric consistency. By incorporating a motion trend module and a latentbone encoder, we explicitly model pose-dependent deformations and temporal variations in clothing behavior. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach in capturing fine-grained clothing deformations and motion-driven shape variations. Our method significantly enhances structural fidelity and perceptual quality in dynamic human reconstruction, particularly in non-rigid regions, while achieving better consistency across temporal frames.
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