Wild2Avatar: Rendering Humans Behind Occlusions
Tiange Xiang Adam Sun Scott Delp Kazuki Kozuka Li Fei-Fei Ehsan Adeli
Rendering the visual appearance of moving humans from occluded monocular
videos is a challenging task. Most existing research renders 3D humans under
ideal conditions, requiring a clear and unobstructed scene. Those methods
cannot be used to render humans in real-world scenes where obstacles may block
the camera's view and lead to partial occlusions. In this work, we present
Wild2Avatar, a neural rendering approach catered for occluded in-the-wild
monocular videos. We propose occlusion-aware scene parameterization for
decoupling the scene into three parts - occlusion, human, and background.
Additionally, extensive objective functions are designed to help enforce the
decoupling of the human from both the occlusion and the background and to
ensure the completeness of the human model. We verify the effectiveness of our
approach with experiments on in-the-wild videos.