LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929286097666048 |
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| author | Xie, Kevin Lorraine, Jonathan Cao, Tianshi Gao, Jun Lucas, James Torralba, Antonio Fidler, Sanja Zeng, Xiaohui |
| author_facet | Xie, Kevin Lorraine, Jonathan Cao, Tianshi Gao, Jun Lucas, James Torralba, Antonio Fidler, Sanja Zeng, Xiaohui |
| contents | Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D optimize multiple prompts simultaneously to improve efficiency, enabling fast text-to-3D synthesis. However, they cannot capture high-frequency geometry and texture details and struggle to scale to large prompt sets, so they generalize poorly. We introduce LATTE3D, addressing these limitations to achieve fast, high-quality generation on a significantly larger prompt set. Key to our method is 1) building a scalable architecture and 2) leveraging 3D data during optimization through 3D-aware diffusion priors, shape regularization, and model initialization to achieve robustness to diverse and complex training prompts. LATTE3D amortizes both neural field and textured surface generation to produce highly detailed textured meshes in a single forward pass. LATTE3D generates 3D objects in 400ms, and can be further enhanced with fast test-time optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15385 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis Xie, Kevin Lorraine, Jonathan Cao, Tianshi Gao, Jun Lucas, James Torralba, Antonio Fidler, Sanja Zeng, Xiaohui Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning 68T45 I.2.6; I.2.7; I.3.6; I.3.7 Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D optimize multiple prompts simultaneously to improve efficiency, enabling fast text-to-3D synthesis. However, they cannot capture high-frequency geometry and texture details and struggle to scale to large prompt sets, so they generalize poorly. We introduce LATTE3D, addressing these limitations to achieve fast, high-quality generation on a significantly larger prompt set. Key to our method is 1) building a scalable architecture and 2) leveraging 3D data during optimization through 3D-aware diffusion priors, shape regularization, and model initialization to achieve robustness to diverse and complex training prompts. LATTE3D amortizes both neural field and textured surface generation to produce highly detailed textured meshes in a single forward pass. LATTE3D generates 3D objects in 400ms, and can be further enhanced with fast test-time optimization. |
| title | LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning 68T45 I.2.6; I.2.7; I.3.6; I.3.7 |
| url | https://arxiv.org/abs/2403.15385 |