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。

TemporalUV：用时间连贯的 UV 坐标捕捉宽松服装