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arXiv · 2306.07349

ATT3D: Amortized Text-to-3D Object Synthesis

Jonathan Lorraine·Kevin Xie·Xiaohui Zeng·Chen-Hsuan Lin·Towaki Takikawa·Nicholas Sharp·Tsung-Yi Lin·Ming-Yu Liu·Sanja Fidler·James Lucas
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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.

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Jonathan Lorraine, Kevin Xie, Xiaohui Zeng, Chen-Hsuan Lin, Towaki Takikawa, Nicholas Sharp, Tsung-Yi Lin, Ming-Yu Liu, Sanja Fidler, James Lucas. 2023-06-06. ATT3D: Amortized Text-to-3D Object Synthesis. https://arxiv.org/abs/2306.07349

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