  Monocular 3D human performance capture is indispensable for many applications
in computer graphics and vision for enabling immersive experiences. However,
detailed capture of humans requires tracking of multiple aspects, including the
skeletal pose, the dynamic surface, which includes clothing, hand gestures as
well as facial expressions. No existing monocular method allows joint tracking
of all these components. To this end, we propose HiFECap, a new neural human
performance capture approach, which simultaneously captures human pose,
clothing, facial expression, and hands just from a single RGB video. We
demonstrate that our proposed network architecture, the carefully designed
training strategy, and the tight integration of parametric face and hand models
to a template mesh enable the capture of all these individual aspects.
Importantly, our method also captures high-frequency details, such as deforming
wrinkles on the clothes, better than the previous works. Furthermore, we show
that HiFECap outperforms the state-of-the-art human performance capture
approaches qualitatively and quantitatively while for the first time capturing
all aspects of the human.
