ATT3D: Amortized Text-To-3D Object Synthesis

Text-to-3D modeling 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 sharing of knowledge between prompts to generalize to unseen setups and smooth interpolations between text for novel assets and simple animations.


Jonathan Lorraine (NVIDIA)
Kevin Xie (NVIDIA)
Xiaohui Zeng (NVIDIA)
Nicholas Sharp (NVIDIA)
Sanja Fidler (NVIDIA)
James Lucas (NVIDIA)

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