2DGS-Avatar：基于2D Gaussian Splatting的可动高保真穿衣虚拟人
2DGS-Avatar: Animatable High-fidelity Clothed Avatar via 2D Gaussian Splatting
AI总结：
提出2DGS-Avatar，基于2D Gaussian Splatting从单目视频建模高保真可动穿衣虚拟人，兼顾快速训练与实时渲染，在AvatarRex和THuman4.0数据集上取得优异性能。
摘要：
从单目视频中实时渲染高保真且可动的虚拟人仍是计算机视觉和图形学中的挑战。过去几年，Neural Radiance Field (NeRF)在渲染质量上取得显著进展，但体积渲染效率低导致运行时性能差。近期基于3D Gaussian Splatting (3DGS)的方法在快速训练和实时渲染方面展现出巨大潜力，但仍受不精确几何造成的伪影困扰。为解决这些问题，我们提出2DGS-Avatar，一种基于2D Gaussian Splatting (2DGS)的新方法，用于建模高保真、训练快速的穿衣可动虚拟人。给定单目RGB视频作为输入，我们的方法生成可由姿态驱动并实时渲染的虚拟人。与基于3DGS的方法相比，2DGS-Avatar在保持快速训练和渲染优势的同时，还能捕捉细致、动态且照片级真实的外观。我们在AvatarRex和THuman4.0等流行数据集上进行大量实验，定性和定量指标均展现出令人印象深刻的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
