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We propose a novel approach to generate temporally coherent UV coordinatesfor loose clothing. Our method is not constrained by human body outlines andcan capture loose garments and hair. We implemented a differentiable pipelineto learn UV mapping between a sequence of RGB inputs and textures via UVcoordinates. Instead of treating the UV coordinates of each frame separately,our data generation approach connects all UV coordinates via feature matchingfor temporal stability. Subsequently, a generative model is trained to balancethe spatial quality and temporal stability. It is driven by supervised andunsupervised losses in both UV and image spaces. Our experiments show that thetrained 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 beused to flexibly synthesize new looks and modified visual styles. Compared toexisting methods, our approach reduces the computational workload to animatenew outfits by several orders of magnitude.

TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates