Summary of Iv-mixed Sampler: Leveraging Image Diffusion Models For Enhanced Video Synthesis, by Shitong Shao et al.
IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis
by Shitong Shao, Zikai Zhou, Lichen Bai, Haoyi Xiong, Zeke Xie
First submitted to arxiv on: 5 Oct 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 IV-Mixed Sampler algorithm leverages strengths of image diffusion models (IDMs) to assist video diffusion models (VDMs), enhancing performance in video generation and editing tasks. By correctly scaling up computation, visual diffusion models can replicate the success of OpenAI’s Strawberry, improving generation quality and compositional generalization. The paper explores inference scaling laws in VDMs, demonstrating state-of-the-art performance on 4 benchmarks, including UCF-101-FVD, MSR-VTT-FVD, Chronomagic-Bench-150, and Chronomagic-Bench-1649. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video diffusion models (VDMs) can generate high-quality videos, but existing methods have limited success. A new algorithm called IV-Mixed Sampler helps VDMs work better by using image diffusion models (IDMs). This makes video frames look better and keeps the video looking smooth over time. The algorithm does this without needing to be trained again, making it efficient. Experiments show that IV-Mixed Sampler works well, beating other methods on 4 different tests. |
Keywords
» Artificial intelligence » Diffusion » Generalization » Inference » Scaling laws
