  Text-to-3D with diffusion models has 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 diverse 3D assets of high visual quality within 20 seconds, which
is two orders of magnitude faster than previous optimization-based methods that
can take 1 to 10 hours. Our project webpage: https://jiahao.ai/instant3d/.
