Reconstructing 3D Human Pose from RGB-D Data with Occlusions

TL;DR

This paper introduces a novel approach for reconstructing 3D human poses from RGB-D images with occlusions by constraining the solution space using scene context and prior knowledge, leading to more plausible reconstructions.

Contribution

The method uniquely separates modeling occluded and visible body parts, employing neural networks and volume matching to improve accuracy and plausibility in 3D human pose reconstruction.

Findings

Outperforms existing methods on the PROX dataset

Produces more accurate and plausible 3D human reconstructions

Effectively handles occlusions by scene-aware constraints

Abstract

We propose a new method to reconstruct the 3D human body from RGB-D images with occlusions. The foremost challenge is the incompleteness of the RGB-D data due to occlusions between the body and the environment, leading to implausible reconstructions that suffer from severe human-scene penetration. To reconstruct a semantically and physically plausible human body, we propose to reduce the solution space based on scene information and prior knowledge. Our key idea is to constrain the solution space of the human body by considering the occluded body parts and visible body parts separately: modeling all plausible poses where the occluded body parts do not penetrate the scene, and constraining the visible body parts using depth data. Specifically, the first component is realized by a neural network that estimates the candidate region named the "free zone", a region carved out of the open space within which it is safe to search for poses of the invisible body parts without concern for penetration. The second component constrains the visible body parts using the "truncated shadow volume" of the scanned body point cloud. Furthermore, we propose to use a volume matching strategy, which yields better performance than surface matching, to match the human body with the confined region. We conducted experiments on the PROX dataset, and the results demonstrate that our method produces more accurate and plausible results compared with other methods.