2DGS-Avatar: Animatable High-fidelity Clothed Avatar via 2D Gaussian Splatting

• URL: http://arxiv.org/abs/2503.02452v1
• Date: Tue, 04 Mar 2025 09:57:24 GMT
• Title: 2DGS-Avatar: Animatable High-fidelity Clothed Avatar via 2D Gaussian Splatting
• Authors: Qipeng Yan, Mingyang Sun, Lihua Zhang,
• Abstract summary: We propose 2DGS-Avatar, a novel approach for modeling animatable clothed avatars with high-fidelity and fast training performance.<n>Our method generates an avatar that can be driven by poses and rendered in real-time.<n>Compared to 3DGS-based methods, our 2DGS-Avatar retains the advantages of fast training and rendering while also capturing detailed, dynamic, and photo-realistic appearances.
• Score: 10.935483693282455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time rendering of high-fidelity and animatable avatars from monocular videos remains a challenging problem in computer vision and graphics. Over the past few years, the Neural Radiance Field (NeRF) has made significant progress in rendering quality but behaves poorly in run-time performance due to the low efficiency of volumetric rendering. Recently, methods based on 3D Gaussian Splatting (3DGS) have shown great potential in fast training and real-time rendering. However, they still suffer from artifacts caused by inaccurate geometry. To address these problems, we propose 2DGS-Avatar, a novel approach based on 2D Gaussian Splatting (2DGS) for modeling animatable clothed avatars with high-fidelity and fast training performance. Given monocular RGB videos as input, our method generates an avatar that can be driven by poses and rendered in real-time. Compared to 3DGS-based methods, our 2DGS-Avatar retains the advantages of fast training and rendering while also capturing detailed, dynamic, and photo-realistic appearances. We conduct abundant experiments on popular datasets such as AvatarRex and THuman4.0, demonstrating impressive performance in both qualitative and quantitative metrics.

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