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。

DLCA-Recon: 动态宽松服装虚拟化重建

DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular  Videos

We propose a novel approach to generate temporally coherent UV coordinates
for loose clothing. Our method is not constrained by human body outlines and
can capture loose garments and hair. We implemented a differentiable pipeline
to learn UV mapping between a sequence of RGB inputs and textures via UV
coordinates. Instead of treating the UV coordinates of each frame separately,
our data generation approach connects all UV coordinates via feature matching
for temporal stability. Subsequently, a generative model is trained to balance
the spatial quality and temporal stability. It is driven by supervised and
unsupervised losses in both UV and image spaces. Our experiments show that the
trained models output high-quality UV coordinates and generalize to new poses.
Once a sequence of UV coordinates has been inferred by our model, it can be
used to flexibly synthesize new looks and modified visual styles. Compared to
existing methods, our approach reduces the computational workload to animate
new outfits by several orders of magnitude.

我们提出了一种新方法，用于生成松散衣服的时间连续的 UV 坐标，该方法可以捕捉松散的服装和头发，通过不同类型的可微分管道来学习图像和纹理之间的 UV 映射。相对于现有的方法，我们的方法能够减少动画新套装的计算工作量数倍，并且经证实其训练出来的模型输出高质量的 UV 坐标并适用于新的姿势。