Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model

TL;DR

Instant3D introduces a fast, feed-forward approach to generate high-quality, diverse 3D assets from text prompts by combining sparse-view image generation with a transformer-based NeRF reconstruction, significantly reducing inference time.

Contribution

The paper presents a novel two-stage method that efficiently produces high-quality 3D models from text, overcoming limitations of previous slow or low-quality approaches.

Findings

Generates diverse 3D assets in 20 seconds

Achieves high visual quality comparable to optimization-based methods

Outperforms prior methods in speed by two orders of magnitude

Abstract

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/.