DeepCap: Monocular Human Performance Capture Using Weak Supervision

• URL: http://arxiv.org/abs/2003.08325v1
• Date: Wed, 18 Mar 2020 16:39:56 GMT
• Title: DeepCap: Monocular Human Performance Capture Using Weak Supervision
• Authors: Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt
• Abstract summary: We propose a novel deep learning approach for monocular dense human performance capture. Our method is trained in a weakly supervised manner based on multi-view supervision. Our approach outperforms the state of the art in terms of quality and robustness.
• Score: 106.50649929342576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality. Many previous performance capture approaches either required expensive multi-view setups or did not recover dense space-time coherent geometry with frame-to-frame correspondences. We propose a novel deep learning approach for monocular dense human performance capture. Our method is trained in a weakly supervised manner based on multi-view supervision completely removing the need for training data with 3D ground truth annotations. The network architecture is based on two separate networks that disentangle the task into a pose estimation and a non-rigid surface deformation step. Extensive qualitative and quantitative evaluations show that our approach outperforms the state of the art in terms of quality and robustness.

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