  Rendering dynamic 3D human from monocular videos is crucial for various
applications such as virtual reality and digital entertainment. Most methods
assume the people is in an unobstructed scene, while various objects may cause
the occlusion of body parts in real-life scenarios. Previous method utilizing
NeRF for surface rendering to recover the occluded areas, but it requiring more
than one day to train and several seconds to render, failing to meet the
requirements of real-time interactive applications. To address these issues, we
propose OccGaussian based on 3D Gaussian Splatting, which can be trained within
6 minutes and produces high-quality human renderings up to 160 FPS with
occluded input. OccGaussian initializes 3D Gaussian distributions in the
canonical space, and we perform occlusion feature query at occluded regions,
the aggregated pixel-align feature is extracted to compensate for the missing
information. Then we use Gaussian Feature MLP to further process the feature
along with the occlusion-aware loss functions to better perceive the occluded
area. Extensive experiments both in simulated and real-world occlusions,
demonstrate that our method achieves comparable or even superior performance
compared to the state-of-the-art method. And we improving training and
inference speeds by 250x and 800x, respectively. Our code will be available for
research purposes.
