ATT3D: Amortized Text-to-3D Object Synthesis
Authors: name: Jonathan Lorraine; name: Kevin Xie; name: Xiaohui Zeng; name: Chen-Hsuan Lin; name: Towaki Takikawa; name: Nicholas Sharp; name: Tsung-Yi Lin; name: Ming-Yu Liu; name: Sanja Fidler; name: James Lucas
Published: 2023-06-06 17:59:10

Abstract: 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.