Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode
  Multi-view Latent Diffusion
Authors: name: Xinyang Li, name: Zhangyu Lai, name: Linning Xu, name: Jianfei Guo, name: Liujuan Cao, name: Shengchuan Zhang, name: Bo Dai, name: Rongrong Ji

We present Dual3D, a novel text-to-3D generation framework that generates
high-quality 3D assets from texts in only 1 minute.The key component is a
dual-mode multi-view latent diffusion model. Given the noisy multi-view
latents, the 2D mode can efficiently denoise them with a single latent
denoising network, while the 3D mode can generate a tri-plane neural surface
for consistent rendering-based denoising. Most modules for both modes are tuned
from a pre-trained text-to-image latent diffusion model to circumvent the
expensive cost of training from scratch. To overcome the high rendering cost
during inference, we propose the dual-mode toggling inference strategy to use
only 1/10 denoising steps with 3D mode, successfully generating a 3D asset in
just 10 seconds without sacrificing quality. The texture of the 3D asset can
be further enhanced by our efficient texture refinement process in a short
time. Extensive experiments demonstrate that our method delivers
state-of-the-art performance while significantly reducing generation time. Our
project page is available at https://dual3d.github.io