ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References
This addresses the problem of monocular 3D human motion capture for applications requiring accurate tracking of difficult performances, but it appears incremental as it builds on existing template-based and reference-aided methods.
The paper tackles the problem of capturing challenging 3D human motions from a single RGB camera, which suffers from complex patterns and self-occlusion, and proposes ChallenCap, a template-based approach using multi-modal references, demonstrating effectiveness and robustness in experiments on a new dataset.
Capturing challenging human motions is critical for numerous applications, but it suffers from complex motion patterns and severe self-occlusion under the monocular setting. In this paper, we propose ChallenCap -- a template-based approach to capture challenging 3D human motions using a single RGB camera in a novel learning-and-optimization framework, with the aid of multi-modal references. We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner. We further adopt a robust motion optimization stage to increase the tracking accuracy, by jointly utilizing the learned motion details from the supervised multi-modal references as well as the reliable motion hints from the input image reference. Extensive experiments on our new challenging motion dataset demonstrate the effectiveness and robustness of our approach to capture challenging human motions.