Summary of Compress3d: a Compressed Latent Space For 3d Generation From a Single Image, by Bowen Zhang et al.
Compress3D: a Compressed Latent Space for 3D Generation from a Single Image
by Bowen Zhang, Tianyu Yang, Yu Li, Lei Zhang, Xi Zhao
First submitted to arxiv on: 20 Mar 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 This paper presents a novel triplane autoencoder that efficiently compresses 3D geometry and texture information, enabling the generation of high-quality 3D assets from a single image. The proposed method introduces a 3D-aware cross-attention mechanism within an autoencoder framework to enhance representation capacity. A diffusion model is trained on this refined latent space, which simultaneously utilizes both image embedding and shape embedding as conditions. Our approach outperforms state-of-the-art algorithms, achieving superior performance with reduced training data and time requirements. Specifically, our method generates high-quality 3D assets in mere seconds (7 seconds) on a single A100 GPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create detailed 3D models from a single picture. The team developed a new way to compress information about both the 3D shape and texture of an object into a small space, making it easier to generate high-quality 3D assets. They also introduced a special technique that allows their method to use both the image and shape information to create even better results. This new approach is faster and more accurate than current methods, taking just 7 seconds to produce high-quality models on a single computer. |
Keywords
» Artificial intelligence » Autoencoder » Cross attention » Diffusion model » Embedding » Latent space
