Text-to-3D with diffusion models have achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from slow inference, low diversity and Janus problems, or are feed-forward methods that generate low quality results due to the scarcity of 3D training data. In this paper, we propose Instant3D, a novel method that generates high-quality and diverse 3D assets from text prompts in a feed-forward manner. We adopt a two-stage paradigm, which first generates a sparse set of four structured and consistent views from text in one shot with a fine-tuned 2D text-to-image diffusion model, and then directly regresses the NeRF from the generated images with a novel transformer-based sparse-view reconstructor. Through extensive experiments, we demonstrate that our method can generate high-quality, diverse and Janus-free 3D assets within 20 seconds, which is two order of magnitude faster than previous optimization-based methods that can take 1 to 10 hours. Our project webpage: https://jiahao.ai/instant3d/.

通过使用两阶段的方法——首先使用精调的2D文本到图像扩散模型一次性生成稀疏一致的四个结构化视图，然后利用一种新颖的基于Transformer的稀疏视图重构器直接回归生成的图像的NeRF——我们提出了Instant3D，一种新颖的方法，以前馈方式从文本提示中生成高质量、多样化的3D资产。通过大量实验证明，我们的方法可以在20秒内生成高质量、多样化且无Janus问题的3D资产，比之前需要1到10小时的基于优化的方法快两个数量级。

Instant3D：稀疏视图生成和大型重建模型下的快速文本到3D