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
Organization: NVIDIA

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.