Summary of Motiongs: Exploring Explicit Motion Guidance For Deformable 3d Gaussian Splatting, by Ruijie Zhu et al.
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
by Ruijie Zhu, Yanzhe Liang, Hanzhi Chang, Jiacheng Deng, Jiahao Lu, Wenfei Yang, Tianzhu Zhang, Yongdong Zhang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 The proposed MotionGS framework is a novel approach to dynamic scene reconstruction in 3D vision. It addresses the limitations of previous methods by incorporating explicit motion priors to guide the deformation of 3D Gaussians. This is achieved through an optical flow decoupling module, which separates camera movement from object motion, and a camera pose refinement module that iteratively optimizes 3D Gaussians and camera poses. The framework demonstrates significant superiority over state-of-the-art methods in both qualitative and quantitative results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MotionGS is a new way to make 3D videos from still images. This is important because it can help us understand how things move and change over time. To do this, the system uses special maps called Gaussians that can stretch and move to match what’s happening in the scene. The system also corrects for camera movements by adjusting its position. This makes the results look much better than previous methods. |
Keywords
* Artificial intelligence * Optical flow
