Summary of Swifttry: Fast and Consistent Video Virtual Try-on with Diffusion Models, by Hung Nguyen et al.
SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models
by Hung Nguyen, Quang Qui-Vinh Nguyen, Khoi Nguyen, Rang Nguyen
First submitted to arxiv on: 13 Dec 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 This paper proposes a new approach to virtual try-on in videos, where a person is wearing a specified garment while maintaining spatiotemporal consistency. The current state-of-the-art methods have limitations when extended from images to videos, leading to inconsistencies. To address this challenge, the authors reconceptualize video virtual try-on as a conditional video inpainting task and enhance image diffusion models with temporal attention layers for improved temporal coherence. They also introduce ShiftCaching, a technique that reduces computational overhead while maintaining consistency. The approach is evaluated on the newly introduced TikTokDress dataset, which features complex backgrounds, challenging movements, and higher resolution than existing datasets. The results show that their method outperforms current baselines in terms of video consistency and inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create a new video where someone wears a specific outfit without any mistakes. Right now, it’s hard to make this happen when we’re working with videos instead of just images. To fix this problem, the authors came up with a new way to think about video virtual try-on and improved some existing models to make them better at keeping track of time. They also invented a new technique that makes their approach faster while still being accurate. The authors tested their method on a new dataset they created, which has more challenging scenes than other datasets. Overall, their approach works really well and is much faster than what’s currently available. |
Keywords
» Artificial intelligence » Attention » Inference » Spatiotemporal
