Summary of Vfusion3d: Learning Scalable 3d Generative Models From Video Diffusion Models, by Junlin Han et al.
VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models
by Junlin Han, Filippos Kokkinos, Philip Torr
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 The proposed VFusion3D model utilizes pre-trained video diffusion models to build scalable 3D generative models. The primary challenge in developing foundation 3D generative models is the limited availability of 3D data, which differs significantly from vast quantities of other types of data. To address this issue, the authors propose using a video diffusion model as a knowledge source for 3D data and fine-tuning its multi-view generative capabilities to generate synthetic 3D datasets. The resulting VFusion3D model can generate 3D assets from single images in seconds and achieves superior performance compared to current state-of-the-art (SOTA) feed-forward 3D generative models, with users preferring the results over 90% of the time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make 3D models using video. The problem is that there isn’t much 3D data available, unlike images, texts, or videos. To fix this, the authors use a pre-trained video model and adjust it to generate more 3D data. This lets them train a new 3D model called VFusion3D, which can create 3D objects from single images in seconds and does better than other similar models. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Fine tuning
