  Despite progress in human motion capture, existing multi-view methods often
face challenges in estimating the 3D pose and shape of multiple closely
interacting people. This difficulty arises from reliance on accurate 2D joint
estimations, which are hard to obtain due to occlusions and body contact when
people are in close interaction. To address this, we propose a novel method
leveraging the personalized implicit neural avatar of each individual as a
prior, which significantly improves the robustness and precision of this
challenging pose estimation task. Concretely, the avatars are efficiently
reconstructed via layered volume rendering from sparse multi-view videos. The
reconstructed avatar prior allows for the direct optimization of 3D poses based
on color and silhouette rendering loss, bypassing the issues associated with
noisy 2D detections. To handle interpenetration, we propose a collision loss on
the overlapping shape regions of avatars to add penetration constraints.
Moreover, both 3D poses and avatars are optimized in an alternating manner. Our
experimental results demonstrate state-of-the-art performance on several public
datasets.
