H2OFlow reconstructs plausible human configurations from a given object point cloud O using point-based representations. A powerful and flexible way to model such goal configurations is through dense flows, which can be applied to both rigid and deformable objects. Dense flows represent how each point transitions from its initial position to its target configuration.
Visual illustration of affordance inference. Given predicted human point clouds, contact affordance assigns high scores to human-object point pairs that are close. Orientational affordances give higher scores to point pairs that yield more uniform cross-product directions (i.e. hand points) and vice versa (i.e. foot points).