Summary of Video Motion Transfer with Diffusion Transformers, by Alexander Pondaven et al.
Video Motion Transfer with Diffusion Transformers
by Alexander Pondaven, Aliaksandr Siarohin, Sergey Tulyakov, Philip Torr, Fabio Pizzati
First submitted to arxiv on: 10 Dec 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 A novel method for transferring motion from a reference video to a newly synthesized one is proposed, specifically designed for Diffusion Transformers (DiT). The approach, called DiTFlow, first analyzes the reference video’s cross-frame attention maps using a pre-trained DiT to extract a patch-wise motion signal. This Attention Motion Flow (AMF) guides the latent denoising process in an optimization-based manner, generating videos that reproduce the motion of the reference one. Additionally, the approach optimizes transformer positional embeddings for improved zero-shot motion transfer capabilities. The method outperforms recent approaches across multiple metrics and human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make videos move like a reference video is developed. It’s called DiTFlow, and it uses special computer models called Diffusion Transformers (DiT) to make this happen. First, the model looks at the reference video and figures out how the motion works between different parts of the video. This information helps create a “motion map” that guides the process of generating new videos. The result is videos that move like the original one, without needing any extra training or data. |
Keywords
» Artificial intelligence » Attention » Diffusion » Optimization » Transformer » Zero shot
