Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode
  Multi-view Latent Diffusion
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
6646b824b4e78ba0e1f73776
Xinyang Li
claimed_verified
2024-05-20T07:38:37.794Z
629631565de6e0eb3292afed
/avatars/0ae1080eebce0f747f650bfc292c46ca.svg
Xinyang Li
user
imlixinyang
6646b824b4e78ba0e1f73777
Zhangyu Lai
6646b824b4e78ba0e1f73778
Linning Xu
6646b824b4e78ba0e1f73779
Jianfei Guo
admin_assigned
2024-05-17T11:04:51.347Z
651507a1fc2301d62d7f32c5
/avatars/1f1066ef5770a591afe0a1172f13125d.svg
guojianfei0228
user
guojianfei
6646b824b4e78ba0e1f7377a
Liujuan Cao
6646b824b4e78ba0e1f7377b
Shengchuan Zhang
6646b824b4e78ba0e1f7377c
Bo Dai
admin_assigned
2024-05-17T11:04:23.420Z
635f93577c05eb9f59966209
/avatars/add48d7e3790a6b500d6c451ef8b0f75.svg
Intelligent Digital Creation
user
BoDai
6646b824b4e78ba0e1f7377d
Rongrong Ji
Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode
  Multi-view Latent Diffusion
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
Dual3D is a text-to-3D generation framework that combines dual-mode latent diffusion models with neural surfaces and pre-trained components for rapid and high-quality 3D asset creation.
dual-mode multi-view latent diffusion model latent denoising network tri-plane neural surface rendering-based denoising dual-mode toggling inference strategy