Summary of Spectral Motion Alignment For Video Motion Transfer Using Diffusion Models, by Geon Yeong Park et al.
Spectral Motion Alignment for Video Motion Transfer using Diffusion Models
by Geon Yeong Park, Hyeonho Jeong, Sang Wan Lee, Jong Chul Ye
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 evolution of diffusion models has revolutionized video generation and understanding. This paper focuses on text-to-video diffusion models (VDMs) that enable the customization of input videos with desired appearances, motions, etc. While advancements have been made, accurate motion information extraction from video frames remains a challenge. Existing methods leverage consecutive frame residuals as target motion vectors but lack global context and are susceptible to frame-wise distortions. To address this, the authors introduce Spectral Motion Alignment (SMA), a novel framework that refines and aligns motion vectors using Fourier and wavelet transforms. SMA learns motion patterns by incorporating frequency-domain regularization, allowing for the learning of whole-frame global motion dynamics while mitigating spatial artifacts. The authors demonstrate SMA’s efficacy in improving motion transfer efficiency and compatibility across various video customization frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video generation has made significant progress thanks to diffusion models. This paper talks about text-to-video models that let us customize videos with specific features, like appearance or motion. While we’ve come a long way, it’s still hard to accurately capture the movement in each frame. Current methods rely on the difference between consecutive frames, but they don’t consider the bigger picture and can be affected by small changes in individual frames. The authors propose a new approach called Spectral Motion Alignment (SMA) that uses special math tools to refine and align motion information. SMA helps learn patterns and whole-frame movement, reducing distortions. The results show that SMA is better at transferring motion and works well with different customization methods. |
Keywords
* Artificial intelligence * Alignment * Diffusion * Regularization
