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We propose DeepMultiCap, a novel method for multi-person performance captureusing sparse multi-view cameras. Our method can capture time varying surfacedetails without the need of using pre-scanned template models. To tackle withthe serious occlusion challenge for close interacting scenes, we combine arecently proposed pixel-aligned implicit function with parametric model forrobust reconstruction of the invisible surface areas. An effectiveattention-aware module is designed to obtain the fine-grained geometry detailsfrom multi-view images, where high-fidelity results can be generated. Inaddition to the spatial attention method, for video inputs, we further proposea novel temporal fusion method to alleviate the noise and temporalinconsistencies for moving character reconstruction. For quantitativeevaluation, we contribute a high quality multi-person dataset, MultiHuman,which consists of 150 static scenes with different levels of occlusions andground truth 3D human models. Experimental results demonstrate thestate-of-the-art performance of our method and the well generalization to realmultiview video data, which outperforms the prior works by a large margin.

DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras