Magic3D is a new text-to-3D content creation tool that
creates 3D mesh models with unprecedented quality.
Together with image conditioning techniques as well as
prompt-based editing approach, we provide users with new ways to control
3D synthesis, opening up new avenues to various creative applications.
Our latest text-to-3D models will be available through NVIDIA Picasso, our generative AI cloud service.
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Abstract
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supervision on NeRF, leading to low-quality 3D models with a long processing time.
In this paper, we address these limitations by utilizing a two-stage optimization framework.
First, we obtain a coarse model using a low-resolution diffusion prior and accelerate with a sparse 3D hash grid structure.
Using the coarse representation as the initialization, we further optimize a textured 3D mesh model with an efficient differentiable renderer interacting with a high-resolution latent diffusion model.
Our method, dubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is 2× faster than DreamFusion (reportedly taking 1.5 hours on average), while also achieving higher resolution.
User studies show 61.7% raters to prefer our approach over DreamFusion.
Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications.
Video
High-Resolution 3D Meshes
Magic3D can create high-quality 3D textured mesh models from input text prompts.
It utilizes a coarse-to-fine strategy leveraging both low- and high-resolution diffusion priors for learning the 3D representation of the target content.
Magic3D synthesizes 3D content with 8× higher-resolution supervision than DreamFusion while also being 2× faster.
[...] indicates helper captions added to improve quality, e.g. "A DSLR photo of".
Given a coarse model generated with a base text prompt, we can modify parts of the text in the prompt, and then fine-tune the NeRF and 3D mesh models to obtain an edited high-resolution 3D mesh.
A squirrel wearing a leather jacket riding a motorcycle.
A bunny riding a scooter.
A fairy riding a bike.
A steampunk squirrel riding a horse.
A baby bunny sitting on top of a stack of pancakes.
A lego bunny sitting on top of a stack of books.
A metal bunny sitting on top of a stack of broccoli.
A metal bunny sitting on top of a stack of chocolate cookies.
Other Editing Capabilities
Given input images for a subject instance, we can fine-tune the diffusion models with DreamBooth and optimize the 3D models with the given prompts.
The identity of the subject can be well-preserved in the 3D models.
We can also condition the diffusion model (eDiff-I) on an input image to transfer its style to the output 3D model.
Approach
We utilize a two-stage coarse-to-fine optimization framework for fast and high-quality text-to-3D content creation.
In the first stage, we obtain a coarse model using a low-resolution diffusion prior and accelerate this with a hash grid and sparse acceleration structure.
In the second stage, we use a textured mesh model initialized from the coarse neural representation, allowing optimization with an efficient differentiable renderer interacting with a high-resolution latent diffusion model.
Presentation
Poster
(Click image to enlarge)
Citation
@inproceedings{lin2023magic3d,
title={Magic3D: High-Resolution Text-to-3D Content Creation},
author={Lin, Chen-Hsuan and Gao, Jun and Tang, Luming and Takikawa, Towaki and Zeng, Xiaohui and Huang, Xun and Kreis, Karsten and Fidler, Sanja and Liu, Ming-Yu and Lin, Tsung-Yi},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition ({CVPR})},
year={2023}
}
