具有结构意识的细粒度高斯点散布用于表现力Avatar重建
Structure-Aware Fine-Grained Gaussian Splatting for Expressive Avatar Reconstruction
AI总结：
本文提出SFGS方法，通过空间三平面和时间六平面捕捉动态特征，结合结构感知高斯模块和残差细化模块，实现高保真全身体素Avatar重建，优于现有方法。
摘要：
从单目视频重建逼真且拓扑感知的人类Avatar仍是在计算机视觉和图形学领域的重要挑战。尽管现有3D人类Avatar建模方法能有效捕捉身体运动，但往往无法准确建模如手部运动和面部表情等细节。为此，我们提出Structure-aware Fine-grained Gaussian Splatting (SFGS)，一种从单目视频序列中重建表现力和一致的全身体素Avatar的新方法。SFGS使用空间-only三平面和时间-aware六平面来捕捉连续帧中的动态特征。设计了结构感知高斯模块，以空间一致的方式捕捉姿态依赖的细节，并提高姿态和纹理表达。为更好地建模手部变形，我们还提出基于细粒度手部重建的残差细化模块。我们的方法仅需单阶段训练，并在定量和定性评估中优于现有最先进基线，生成具有自然运动和细粒度细节的高保真Avatar。代码在GitHub上：https://github.com/Su245811YZ/SFGS
英文摘要：
Reconstructing photorealistic and topology-aware human avatars from monocular videos remains a significant challenge in the fields of computer vision and graphics. While existing 3D human avatar modeling approaches can effectively capture body motion, they often fail to accurately model fine details such as hand movements and facial expressions. To address this, we propose Structure-aware Fine-grained Gaussian Splatting (SFGS), a novel method for reconstructing expressive and coherent full-body 3D human avatars from a monocular video sequence. The SFGS use both spatial-only triplane and time-aware hexplane to capture dynamic features across consecutive frames. A structure-aware gaussian module is designed to capture pose-dependent details in a spatially coherent manner and improve pose and texture expression. To better model hand deformations, we also propose a residual refinement module based on fine-grained hand reconstruction. Our method requires only a single-stage training and outperforms state-of-the-art baselines in both quantitative and qualitative evaluations, generating high-fidelity avatars with natural motion and fine details. The code is on Github: https://github.com/Su245811YZ/SFGS
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。
