Summary of Videomv: Consistent Multi-view Generation Based on Large Video Generative Model, by Qi Zuo et al.
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
by Qi Zuo, Xiaodong Gu, Lingteng Qiu, Yuan Dong, Zhengyi Zhao, Weihao Yuan, Rui Peng, Siyu Zhu, Zilong Dong, Liefeng Bo, Qixing Huang
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 presents a novel framework for generating multi-view images based on text or single-image prompts, with contributions to both the data used for training and ensuring multi-view consistency. The approach leverages off-the-shelf video generative models, which employ temporal modules to enforce frame consistency and reduce the train-finetuning domain gap. To enhance multi-view consistency, a 3D-Aware Denoising Sampling module is introduced, involving images rendered from a global 3D model in the denoising loop. The framework can generate dense views, converges faster than state-of-the-art approaches, and outperforms existing methods in both quantitative metrics and visual effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create more realistic 3D content by generating multiple views of an object or scene from a text prompt. It’s like asking a computer to draw a 3D picture of what you describe. The researchers created a new way to do this, using existing video generation models that are good at making sure each frame is consistent with the others. They also developed a special module that helps make the generated images look even more realistic by involving a global 3D model in the process. This approach can generate many views quickly and accurately, and it’s better than previous methods in terms of both how well it works and how good the results look. |
Keywords
» Artificial intelligence » Prompt
