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arXiv · 1810.05340

4D Human Body Correspondences from Panoramic Depth Maps

Zhong Li·Minye Wu·Wangyiteng Zhou·Jingyi Yu
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Abstract

The availability of affordable 3D full body reconstruction systems has given rise to free-viewpoint video (FVV) of human shapes. Most existing solutions produce temporally uncorrelated point clouds or meshes with unknown point/vertex correspondences. Individually compressing each frame is ineffective and still yields to ultra-large data sizes. We present an end-to-end deep learning scheme to establish dense shape correspondences and subsequently compress the data. Our approach uses sparse set of "panoramic" depth maps or PDMs, each emulating an inward-viewing concentric mosaics. We then develop a learning-based technique to learn pixel-wise feature descriptors on PDMs. The results are fed into an autoencoder-based network for compression. Comprehensive experiments demonstrate our solution is robust and effective on both public and our newly captured datasets.

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Zhong Li, Minye Wu, Wangyiteng Zhou, Jingyi Yu. 2018-10-12. 4D Human Body Correspondences from Panoramic Depth Maps. https://arxiv.org/abs/1810.05340

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