  Most existing human rendering methods require every part of the human to be
fully visible throughout the input video. However, this assumption does not
hold in real-life settings where obstructions are common, resulting in only
partial visibility of the human. Considering this, we present OccFusion, an
approach that utilizes efficient 3D Gaussian splatting supervised by pretrained
2D diffusion models for efficient and high-fidelity human rendering. We propose
a pipeline consisting of three stages. In the Initialization stage, complete
human masks are generated from partial visibility masks. In the Optimization
stage, 3D human Gaussians are optimized with additional supervision by
Score-Distillation Sampling (SDS) to create a complete geometry of the human.
Finally, in the Refinement stage, in-context inpainting is designed to further
improve rendering quality on the less observed human body parts. We evaluate
OccFusion on ZJU-MoCap and challenging OcMotion sequences and find that it
achieves state-of-the-art performance in the rendering of occluded humans.
