Summary of Ghnerf: Learning Generalizable Human Features with Efficient Neural Radiance Fields, by Arnab Dey et al.
GHNeRF: Learning Generalizable Human Features with Efficient Neural Radiance Fields
by Arnab Dey, Di Yang, Rohith Agaram, Antitza Dantcheva, Andrew I. Comport, Srinath Sridhar, Jean Martinet
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advances in Neural Radiance Fields (NeRF) have demonstrated promising results in 3D scene representations, including 3D human representations. However, these representations often lack crucial information on the underlying human pose and structure, which is essential for AR/VR applications and games. This paper introduces GHNeRF, a novel approach that combines NeRF representation with pre-trained 2D encoders to learn joint locations of human subjects. The method uses biomechanic features, such as joint locations, along with human geometry and texture, allowing simultaneous learning of these attributes. To evaluate the effectiveness of GHNeRF, the authors conduct a comprehensive comparison with state-of-the-art human NeRF techniques and joint estimation algorithms. Results show that GHNeRF achieves state-of-the-art results in near real-time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to create a 3D model of a person without knowing their posture or body shape. That’s what’s been happening with current methods for creating 3D humans using Neural Radiance Fields (NeRF). This paper introduces a new way, called GHNeRF, that combines NeRF with special computer vision techniques to learn the joints and structure of human bodies. By doing so, it can create more accurate and detailed 3D models of people in real-time. |
