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/.

翻译：近年来，基于扩散模型的文本到三维方法取得了显著进展。然而，现有方法要么依赖基于分数蒸馏的优化方法，存在推理速度慢、多样性低和Janus问题，要么是前馈方法，因三维训练数据稀缺而生成质量较低。本文提出Instant3D，一种以前馈方式从文本提示生成高质量且多样化三维资产的新方法。我们采用两阶段范式：首先通过微调的二维文本到图像扩散模型，一次性从文本生成稀疏的四个结构化一致视图，随后利用新型基于Transformer的稀疏视图重建器，直接从生成的图像回归NeRF。通过大量实验证明，我们的方法能在20秒内生成高质量、多样化且无Janus问题的三维资产，比以往需要1至10小时的基于优化的方法快两个数量级。项目网址：https://jiahao.ai/instant3d/。