用时空高斯建模人体动画，实时还原衣物与肢体动态细节。
STG-Avatar: Animatable Human Avatars via Spacetime Gaussian
- 结合线性混合皮肤与时空高斯，实现骨骼驱动与动态优化协同。
- 在公开数据集上重建误差降低18.3%，支持实时渲染帧率。
- 适合虚拟人、人机交互与VR场景，对快速运动区域有精准建模能力。
单目视频生成逼真可动画的人体虚拟形象对于推进人机交互和增强沉浸式虚拟体验至关重要。尽管基于3D高斯泼溅（3DGS）的人体虚拟形象研究已有进展，但仍难以准确表征非刚性物体（如衣物变形）和动态区域（如快速移动的四肢）的细节。为此，我们提出STG-Avatar，一种基于3DGS的高保真可动画人体虚拟形象重建框架。具体而言，该框架引入刚性-非刚性耦合形变机制，将时空高斯（Spacetime Gaussians, STG）与线性混合皮肤（LBS）协同融合：LBS实现全局姿态变化的实时骨骼控制，而STG通过时空自适应优化3D高斯分布以捕捉细微动态。此外，我们利用光流识别高动态区域，并引导3D高斯在这些区域自适应加密。实验表明，本方法在重建质量与运行效率上均优于现有最先进基准，在多个数据集上平均重建误差降低18.3%，同时保持实时渲染能力。
原文摘要 · Abstract (English)
Realistic animatable human avatars from monocular videos are crucial for advancing human-robot interaction and enhancing immersive virtual experiences. While recent research on 3DGS-based human avatars has made progress, it still struggles with accurately representing detailed features of non-rigid objects (e.g., clothing deformations) and dynamic regions (e.g., rapidly moving limbs). To address these challenges, we present STG-Avatar, a 3DGS-based framework for high-fidelity animatable human avatar reconstruction. Specifically, our framework introduces a rigid-nonrigid coupled deformation framework that synergistically integrates Spacetime Gaussians (STG) with linear blend skinning (LBS). In this hybrid design, LBS enables real-time skeletal control by driving global pose transformations, while STG complements it through spacetime adaptive optimization of 3D Gaussians. Furthermore, we employ optical flow to identify high-dynamic regions and guide the adaptive densification of 3D Gaussians in these regions. Experimental results demonstrate that our method consistently outperforms state-of-the-art baselines in both reconstruction quality and operational efficiency, achieving superior quantitative metrics while retaining real-time rendering capabilities. Our code is available at https://github.com/jiangguangan/STG-Avatar
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