Efficient Rendering of Occluded Humans using 3D Gaussian Splatting

Core Concepts

OccGaussian, a novel method for rendering high-quality humans in monocular videos with occlusions, achieves fast training (6-13 minutes) and real-time rendering (up to 169 FPS) by leveraging 3D Gaussian Splatting.

Abstract

The paper proposes OccGaussian, a method for rendering dynamic 3D humans from monocular videos with occlusions. The key contributions are:

OccGaussian is the first work that applies 3D Gaussian Splatting to render occluded humans, enabling rapid training (6-13 minutes) and real-time rendering (up to 169 FPS).

It introduces an occlusion feature query mechanism that extracts aggregated pixel-aligned features from the K-nearest visible points to compensate for the missing information in occluded regions. Additionally, it designs occlusion loss and consistency loss to better handle occluded areas.

Extensive experiments on simulated and real-world occlusion datasets demonstrate that OccGaussian achieves comparable or even superior performance compared to the state-of-the-art OccNeRF method, while significantly improving training and inference speeds by 250x and 800x respectively.

Stats

OccGaussian can train within 6-13 minutes, 250x faster than OccNeRF which requires over 1 day.
OccGaussian can render at up to 169 FPS, 800x faster than OccNeRF which takes several seconds per image.

Quotes

"OccGaussian, the first method to render human in monocular videos with occlusions using 3D Gaussian Splatting."
"OccGaussian can train within 6-13 minutes, 250x faster than OccNeRF which requires over 1 day."
"OccGaussian can render at up to 169 FPS, 800x faster than OccNeRF which takes several seconds per image."

Key Insights Distilled From

OccGaussian: 3D Gaussian Splatting for Occluded Human Rendering

by Jingrui Ye,Z... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08449.pdf

Deeper Inquiries

How could OccGaussian be extended to handle inaccurate human pose and camera parameters in in-the-wild videos

To handle inaccurate human pose and camera parameters in in-the-wild videos, OccGaussian could incorporate a robust pose estimation algorithm that can adapt to varying conditions. By integrating a pose refinement module that can adjust for inaccuracies in the initial pose estimates, OccGaussian can enhance the accuracy of rendering in challenging scenarios. Additionally, implementing a camera parameter estimation module that can dynamically adjust based on the scene's characteristics can help improve the rendering quality in in-the-wild videos. By leveraging machine learning techniques to predict and refine pose and camera parameters, OccGaussian can better handle inaccuracies and variations in real-world settings.

What other applications beyond human rendering could benefit from the occlusion feature query and specialized loss functions proposed in OccGaussian

The occlusion feature query and specialized loss functions proposed in OccGaussian can benefit various applications beyond human rendering. One such application is object recognition in images with occlusions. By utilizing the occlusion feature query to extract relevant information from visible regions and incorporating specialized loss functions to handle occluded areas, the model can improve object recognition accuracy even when parts of the object are obscured. This can be particularly useful in surveillance systems, autonomous vehicles, and medical imaging where occlusions are common and can impact the accuracy of object detection and recognition algorithms.

Could temporal information be incorporated into OccGaussian to better recover occluded regions that have been obscured for extended periods

Incorporating temporal information into OccGaussian can significantly enhance its ability to recover occluded regions that have been obscured for extended periods. By introducing a temporal consistency module that considers the evolution of occlusions over time, OccGaussian can leverage information from previous frames to predict and fill in occluded regions more accurately. This temporal information can help the model learn patterns of occlusions and improve the continuity and coherence of rendered images over time. By integrating temporal information, OccGaussian can better handle long-term occlusions and produce more realistic renderings in dynamic scenes.