ATT3D: Amortized Text-to-3D Object Synthesis
Text-to-3D modelling has seen exciting progress by combining generative
text-to-image models with image-to-3D methods like Neural Radiance Fields.
DreamFusion recently achieved high-quality results but requires a lengthy,
per-prompt optimization to create 3D objects. To address this, we amortize
optimization over text prompts by training on many prompts simultaneously with
a unified model, instead of separately. With this, we share computation across
a prompt set, training in less time than per-prompt optimization. Our framework
- Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to
generalize to unseen setups and smooth interpolations between text for novel
assets and simple animations.
648940b751b69a8c82ec1f15
Jonathan Lorraine
admin_assigned
2023-06-14T08:41:48.557Z
631b7370bf1351ed2bd0abdc
https://cdn-avatars.huggingface.co/v1/production/uploads/631b7370bf1351ed2bd0abdc/p0ZRMjgp5mt3sT5OhdHsp.png
Jonathan Lorraine
user
lorraine2
648940b751b69a8c82ec1f16
Kevin Xie
claimed_verified
2024-03-25T13:58:24.710Z
6346d26dbcb5e67902ae106d
/avatars/310d2a8141d60ff7634dd6e40efe371e.svg
Kevin Xie
user
kevincxie
648940b751b69a8c82ec1f17
Xiaohui Zeng
claimed_verified
2024-11-16T19:47:30.413Z
63363cb8a8048332fdc268cd
/avatars/a9bf6ce3394a8b76eade1a7517b37fa1.svg
xiaohui zeng
user
xiaohui2022
648940b751b69a8c82ec1f18
Chen-Hsuan Lin
admin_assigned
2023-06-14T08:43:23.447Z
6324e100fee2904e370bc940
/avatars/4bb215bd711452ee6d28894706773009.svg
Chen-Hsuan Lin
user
chenhsuanlin
648940b751b69a8c82ec1f19
Towaki Takikawa
648940b751b69a8c82ec1f1a
Nicholas Sharp
648940b751b69a8c82ec1f1b
Tsung-Yi Lin
admin_assigned
2023-06-14T08:54:06.020Z
63b738acbd2d1535227daa4c
/avatars/23300548e5c50f44e95d63568221f47b.svg
Tsung-Yi Lin
user
tsungyi
648940b751b69a8c82ec1f1c
Ming-Yu Liu
admin_assigned
2023-06-14T08:44:08.862Z
62f049afdf4b93aad5c7f2d6
/avatars/e272e58ad996733d7098e50248e5b57e.svg
Ming-Yu Liu
user
mingyuliutw
648940b751b69a8c82ec1f1d
Sanja Fidler
648940b751b69a8c82ec1f1e
James Lucas
ATT3D: Amortized Text-to-3D Object Synthesis
Text-to-3D modelling has seen exciting progress by combining generative
text-to-image models with image-to-3D methods like Neural Radiance Fields.
DreamFusion recently achieved high-quality results but requires a lengthy,
per-prompt optimization to create 3D objects. To address this, we amortize
optimization over text prompts by training on many prompts simultaneously with
a unified model, instead of separately. With this, we share computation across
a prompt set, training in less time than per-prompt optimization. Our framework
- Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to
generalize to unseen setups and smooth interpolations between text for novel
assets and simple animations.
A framework called Amortized Text-to-3D (ATT3D) efficiently generates 3D objects from text by sharing computation across multiple text prompts and enabling knowledge sharing for novel setups and smooth animations.
text-to-3D modelling generative text-to-image models Neural Radiance Fields DreamFusion amortized optimization unified model prompt set generalization novel assets simple animations