Summary of 3d-adapter: Geometry-consistent Multi-view Diffusion For High-quality 3d Generation, by Hansheng Chen et al.
3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation
by Hansheng Chen, Bokui Shen, Yulin Liu, Ruoxi Shi, Linqi Zhou, Connor Z. Lin, Jiayuan Gu, Hao Su, Gordon Wetzstein, Leonidas Guibas
First submitted to arxiv on: 24 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 paper introduces a new module called 3D-Adapter that enhances the geometry quality of open-domain 3D object generation models. The existing models rely on 2D network architectures that lack inherent 3D biases, leading to compromised geometric consistency. To address this challenge, the authors propose a plug-in module that infuses 3D geometry awareness into pretrained image diffusion models. The module uses 3D feedback augmentation, decoding intermediate multi-view features into a coherent 3D representation and then re-encoding the rendered RGBD views to augment the base model through feature addition. The authors study two variants of 3D-Adapter: a fast feed-forward version based on Gaussian splatting and a versatile training-free version utilizing neural fields and meshes. The results demonstrate that 3D-Adapter greatly enhances the geometry quality of text-to-multi-view models such as Instant3D and Zero123++, and enables high-quality 3D generation using the plain text-to-image Stable Diffusion. The authors also showcase the broad application potential of 3D-Adapter in various tasks, including text-to-3D, image-to-3D, text-to-texture, and text-to-avatar. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make computer-generated 3D objects look more realistic. Right now, most models are made using 2D pictures as a guide, which can lead to poor geometry. To fix this, the authors created a special module called 3D-Adapter that helps pretrained models understand 3D shapes better. The module takes in 2D features and converts them into a 3D representation, then adds that back into the model. The results show that this works really well for generating 3D objects from text or images. |
Keywords
» Artificial intelligence » Diffusion
