LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
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.
6600fc84bcf0790b8d611a6e
Kevin Xie
claimed_verified
2024-03-25T13:58:08.235Z
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Kevin Xie
user
kevincxie
6600fc84bcf0790b8d611a6f
Jonathan Lorraine
admin_assigned
2024-03-25T11:51:21.069Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/631b7370bf1351ed2bd0abdc/p0ZRMjgp5mt3sT5OhdHsp.png
Jonathan Lorraine
user
lorraine2
6600fc84bcf0790b8d611a70
Tianshi Cao
admin_assigned
2024-03-25T11:51:28.050Z
63e07dfaf0c75dfb876d9d71
/avatars/8dab667735111e070cc6d44609dbdaf3.svg
Tianshi Cao
user
jeanlancel
6600fc84bcf0790b8d611a71
Jun Gao
6600fc84bcf0790b8d611a72
James Lucas
6600fc84bcf0790b8d611a73
Antonio Torralba
6600fc84bcf0790b8d611a74
Sanja Fidler
6600fc84bcf0790b8d611a75
Xiaohui Zeng
claimed_verified
2024-11-16T19:47:27.434Z
63363cb8a8048332fdc268cd
/avatars/a9bf6ce3394a8b76eade1a7517b37fa1.svg
xiaohui zeng
user
xiaohui2022
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
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.
LATTE3D generates high-quality 3D objects quickly by using a scalable architecture and 3D-aware diffusion priors.
ATT3D LATTE3D 3D-aware diffusion priors shape regularization neural field textured surface textured meshes