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.

使用 DLCA-Recon 方法，从单目视频中创建人类化身，模拟松散服装的运动并优化变形场，以更准确地重建动态人物表面。