Summary of Hunyuan3d 1.0: a Unified Framework For Text-to-3d and Image-to-3d Generation, by Xianghui Yang et al.
Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation
by Xianghui Yang, Huiwen Shi, Bowen Zhang, Fan Yang, Jiacheng Wang, Hongxu Zhao, Xinhai Liu, Xinzhou Wang, Qingxiang Lin, Jiaao Yu, Lifu Wang, Jing Xu, Zebin He, Zhuo Chen, Sicong Liu, Junta Wu, Yihang Lian, Shaoxiong Yang, Yuhong Liu, Yong Yang, Di Wang, Jie Jiang, Chunchao Guo
First submitted to arxiv on: 4 Nov 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 proposes a two-stage approach called Hunyuan3D 1.0 for efficient 3D generative models that support text- and image-conditioned generation. The first stage employs a multi-view diffusion model to generate high-quality RGB images from different viewpoints in approximately 4 seconds, while the second stage uses a feed-forward reconstruction model to rapidly reconstruct the original 3D asset given the generated images in around 7 seconds. The framework integrates Hunyuan-DiT, a text-to-image model, allowing for both text- and image-conditioned 3D generation. With 3x more parameters than existing models, Hunyuan3D 1.0 achieves an impressive balance between speed and quality, reducing generation time while maintaining diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to generate 3D images that is fast and good-quality. It uses two steps: first, it generates lots of different views of the same 3D object using a special model called Hunyuan-DiT; then, it takes those views and puts them back together into the original 3D shape. This method is much faster than other models, taking only a few seconds to generate, but still looks very realistic. |
Keywords
» Artificial intelligence » Diffusion model
