DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras

• URL: http://arxiv.org/abs/2105.00261v1
• Date: Sat, 1 May 2021 14:32:13 GMT
• Title: DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras
• Authors: Yang Zheng, Ruizhi Shao, Yuxiang Zhang, Tao Yu, Zerong Zheng, Qionghai Dai, Yebin Liu
• Abstract summary: DeepMultiCap is a novel method for multi-person performance capture using sparse multi-view cameras. Our method can capture time varying surface details without the need of using pre-scanned template models.
• Score: 63.186486240525554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose DeepMultiCap, a novel method for multi-person performance capture using sparse multi-view cameras. Our method can capture time varying surface details without the need of using pre-scanned template models. To tackle with the serious occlusion challenge for close interacting scenes, we combine a recently proposed pixel-aligned implicit function with parametric model for robust reconstruction of the invisible surface areas. An effective attention-aware module is designed to obtain the fine-grained geometry details from multi-view images, where high-fidelity results can be generated. In addition to the spatial attention method, for video inputs, we further propose a novel temporal fusion method to alleviate the noise and temporal inconsistencies for moving character reconstruction. For quantitative evaluation, we contribute a high quality multi-person dataset, MultiHuman, which consists of 150 static scenes with different levels of occlusions and ground truth 3D human models. Experimental results demonstrate the state-of-the-art performance of our method and the well generalization to real multiview video data, which outperforms the prior works by a large margin.

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