  Reconstructing interacting hands from monocular RGB data is a challenging
task, as it involves many interfering factors, e.g. self- and mutual occlusion
and similar textures. Previous works only leverage information from a single
RGB image without modeling their physically plausible relation, which leads to
inferior reconstruction results. In this work, we are dedicated to explicitly
exploiting spatial-temporal information to achieve better interacting hand
reconstruction. On one hand, we leverage temporal context to complement
insufficient information provided by the single frame, and design a novel
temporal framework with a temporal constraint for interacting hand motion
smoothness. On the other hand, we further propose an interpenetration detection
module to produce kinetically plausible interacting hands without physical
collisions. Extensive experiments are performed to validate the effectiveness
of our proposed framework, which achieves new state-of-the-art performance on
public benchmarks.
