  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.
