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.

通过将生成式文本转图像模型和图像到 3D 方法相结合，如神经辐射场，文本到 3D 建模已经取得了令人兴奋的进展，但目前需要逐个优化来创建 3D 对象。我们提出了一种新的框架 - 摊销文本到 3D（ATT3D）- 通过以统一模型同时训练多个提示来摊销优化，从而在更短的时间内共享提示集的计算，实现了跨提示的知识共享，可以概括未见过的设置，并使文本之间的插值更加平滑，从而实现了新资产和简单动画的 3D 建模。