用2D高斯点云实现快速高质量可驱动虚拟人建模
2DGS-Avatar: Animatable High-fidelity Clothed Avatar via 2D Gaussian Splatting
- 基于2D高斯点云构建可动画的写实虚拟人
- 训练与渲染速度显著优于3DGS方法，支持实时表现
- 适合虚拟人、游戏、元宇宙等需快速生成写实角色的场景
从单目视频实时渲染高保真且可动画化的虚拟人仍是计算机视觉与图形学中的难题。尽管神经辐射场（NeRF）在渲染质量上取得进展，但其体素渲染效率低导致运行性能差。近期基于3D高斯点云（3DGS）的方法在训练与渲染速度上展现潜力，但仍存在几何不准确引发的伪影问题。为此，我们提出2DGS-Avatar，一种基于2D高斯点云的新方法，用于建模可动画的穿衣虚拟人，具备高保真度与快速训练能力。输入为单目RGB视频，输出可由姿态驱动并实时渲染。相比3DGS方法，2DGS-Avatar在保持快速训练与渲染优势的同时，更精确捕捉动态细节与照片级真实感外观。我们在AvatarRex与THuman4.0等主流数据集上进行了大量实验，定性与定量评估均表现出色。
原文摘要 · Abstract (English)
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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