Summary of Mvdiff: Scalable and Flexible Multi-view Diffusion For 3d Object Reconstruction From Single-view, by Emmanuelle Bourigault and Pauline Bourigault
MVDiff: Scalable and Flexible Multi-View Diffusion for 3D Object Reconstruction from Single-View
by Emmanuelle Bourigault, Pauline Bourigault
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 In this paper, researchers tackle the challenge of generating consistent multiple views for 3D reconstruction tasks using image-to-3D diffusion models. Current approaches often compromise on model speed, generalizability, or quality when incorporating 3D representations. To overcome these limitations, the authors propose a novel framework that leverages scene representation transformers and view-conditioned diffusion models to generate consistent multi-view images from single images or leveraging scene representation. The framework incorporates epipolar geometry constraints and multi-view attention to enforce 3D consistency. Experimental results show that the proposed model can generate 3D meshes surpassing baseline methods in evaluation metrics such as PSNR, SSIM, and LPIPS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a tricky problem in computer vision – making sure multiple views of an object or scene are consistent with each other when reconstructed from a single image. Currently, models that do this job well often trade off between how fast they are, how well they generalize to new situations, and how accurate their results are. The authors came up with a clever way to use special types of computer vision models called transformers and diffusion models to generate multiple views that match each other. They also added some extra tricks to make sure the generated views look realistic. By using just one image as input, their model can create 3D shapes that beat existing methods in terms of quality. |
Keywords
* Artificial intelligence * Attention * Diffusion
