Rendering Humans from Object-Occluded Monocular Videos
This addresses the challenge of rendering occluded humans in real-world monocular videos, which is an incremental improvement over existing neural rendering methods.
The paper tackles the problem of rendering 3D humans from monocular videos with object occlusions, which existing methods fail to handle due to point-point mapping disparities and lack of feasibility criteria. The proposed OccNeRF method achieves better rendering in severely occluded scenes by integrating geometry and visibility priors, demonstrating superiority on simulated and real-world occlusions.
3D understanding and rendering of moving humans from monocular videos is a challenging task. Despite recent progress, the task remains difficult in real-world scenarios, where obstacles may block the camera view and cause partial occlusions in the captured videos. Existing methods cannot handle such defects due to two reasons. First, the standard rendering strategy relies on point-point mapping, which could lead to dramatic disparities between the visible and occluded areas of the body. Second, the naive direct regression approach does not consider any feasibility criteria (ie, prior information) for rendering under occlusions. To tackle the above drawbacks, we present OccNeRF, a neural rendering method that achieves better rendering of humans in severely occluded scenes. As direct solutions to the two drawbacks, we propose surface-based rendering by integrating geometry and visibility priors. We validate our method on both simulated and real-world occlusions and demonstrate our method's superiority.