Summary of Mavin: Multi-action Video Generation with Diffusion Models Via Transition Video Infilling, by Bowen Zhang et al.
MAVIN: Multi-Action Video Generation with Diffusion Models via Transition Video Infilling
by Bowen Zhang, Xiaofei Xie, Haotian Lu, Na Ma, Tianlin Li, Qing Guo
First submitted to arxiv on: 28 May 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 The proposed model, MAVIN, tackles the challenge of generating videos with sequential actions by splicing multiple single-action segments together. This requires generating smooth transitions between segments, which is achieved through several innovative techniques. The first technique involves consecutive noising and variable-length sampling to handle large gaps and varied generation lengths. Another technique, boundary frame guidance (BFG), addresses the lack of semantic guidance during transition generation. Additionally, a Gaussian filter mixer (GFM) manages noise initialization during inference, mitigating train-test discrepancy while preserving generation flexibility. The model’s performance is evaluated using a new metric, CLIP-RS (CLIP Relative Smoothness), which assesses temporal coherence and smoothness in addition to traditional quality-based metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MAVIN is a new way to make videos by combining short actions together. This helps solve a big problem in video generation: making sure the actions flow smoothly from one to another. To do this, MAVIN uses some clever tricks. First, it adds noise to the video and then adjusts the length of the segments being added. It also gives special guidance to the model at the beginning and end of each segment. Finally, it uses a special filter to make sure the transitions between segments are smooth and natural. This is important because traditional metrics only look at how good the individual actions are, but MAVIN cares about how well they work together. |
Keywords
* Artificial intelligence * Inference
