  Automatic perception of human behaviors during social interactions is crucial
for AR/VR applications, and an essential component is estimation of plausible
3D human pose and shape of our social partners from the egocentric view. One of
the biggest challenges of this task is severe body truncation due to close
social distances in egocentric scenarios, which brings large pose ambiguities
for unseen body parts. To tackle this challenge, we propose a novel
scene-conditioned diffusion method to model the body pose distribution.
Conditioned on the 3D scene geometry, the diffusion model generates bodies in
plausible human-scene interactions, with the sampling guided by a physics-based
collision score to further resolve human-scene inter-penetrations. The
classifier-free training enables flexible sampling with different conditions
and enhanced diversity. A visibility-aware graph convolution model guided by
per-joint visibility serves as the diffusion denoiser to incorporate
inter-joint dependencies and per-body-part control. Extensive evaluations show
that our method generates bodies in plausible interactions with 3D scenes,
achieving both superior accuracy for visible joints and diversity for invisible
body parts. The code is available at
https://sanweiliti.github.io/egohmr/egohmr.html.
