Instead of sparse contact points, modeling continuous proximity fields across entire surfaces—guided by learned interaction patterns—enables more realistic and physically plausible 3D human-object reconstruction from images.
This paper tackles 3D human-object interaction reconstruction from single images by introducing InterFields—a dense representation of proximity between body and object surfaces—and LEXIS, a learned discrete manifold of interaction patterns.