DLCA-Recon：从单目视频重建动态宽松衣物人体化身
DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos
AI总结：
提出DLCA-Recon方法，通过规范空间初始化穿衣人体和动态变形场，从单目视频重建动态宽松衣物人体，优于现有方法。
摘要：
重建穿着宽松衣物的人体动态是一个重要但困难的任务。为了解决这一挑战，我们提出了一种名为DLCA-Recon的方法，从单目视频创建人体化身。当人体自由移动和动作时，宽松衣物到其下身体的距离在每一帧中都会迅速变化。以往的方法缺乏有效的几何初始化和约束来指导变形优化以解释这种剧烈变化，导致重建表面不连续和不完整。为了更准确地建模变形，我们提出在规范空间中初始化一个估计的三维穿衣人体，因为变形场从穿衣人体学习比从SMPL学习更容易。结合显式网格和隐式SDF两种表示，我们利用连续帧之间的物理连接信息，提出了一种动态变形场（DDF）来优化变形场。DDF考虑了宽松衣物上的作用力，以增强变形的可解释性，并有效捕捉宽松衣物的自由运动。此外，我们将SMPL蒙皮权重传播到每个个体，并在优化过程中细化姿态和蒙皮权重，以改进蒙皮变换。基于更合理的初始化和DDF，我们可以更准确地模拟真实世界的物理。在公开数据集和我们自己的数据集上的大量实验验证了我们的方法对于穿着宽松衣物的人体能产生优于最先进方法的结果。
英文摘要：
Reconstructing a dynamic human with loose clothing is an important but difficult task. To address this challenge, we propose a method named DLCA-Recon to create human avatars from monocular videos. The distance from loose clothing to the underlying body rapidly changes in every frame when the human freely moves and acts. Previous methods lack effective geometric initialization and constraints for guiding the optimization of deformation to explain this dramatic change, resulting in the discontinuous and incomplete reconstruction surface. To model the deformation more accurately, we propose to initialize an estimated 3D clothed human in the canonical space, as it is easier for deformation fields to learn from the clothed human than from SMPL. With both representations of explicit mesh and implicit SDF, we utilize the physical connection information between consecutive frames and propose a dynamic deformation field (DDF) to optimize deformation fields. DDF accounts for contributive forces on loose clothing to enhance the interpretability of deformations and effectively capture the free movement of loose clothing. Moreover, we propagate SMPL skinning weights to each individual and refine pose and skinning weights during the optimization to improve skinning transformation. Based on more reasonable initialization and DDF, we can simulate real-world physics more accurately. Extensive experiments on public and our own datasets validate that our method can produce superior results for humans with loose clothing compared to the SOTA methods.
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。
