3D-aware Image Generation using 2D Diffusion Models
Jianfeng Xiang1,2
Jiaolong Yang2
Binbin Huang3
Xin Tong2
1Tsinghua University
2Microsoft Research Asia
3ShanghaiTech University
ICCV 2023
In this paper, we introduce a novel 3D-aware image generation method that leverages 2D diffusion models. We formulate the 3D-aware image generation task as multiview 2D image set generation, and further to a sequential unconditional–conditional multiview image generation process. This allows us to utilize 2D diffusion models to boost the generative modeling power of the method. Additionally, we incorporate depth information from monocular depth estimators to construct the training data for the conditional diffusion model using only still images. We train our method on a large-scale dataset, i.e., ImageNet, which is not addressed by previous methods. It produces high-quality images that significantly outperform prior methods. Furthermore, our approach showcases its capability to generate instances with large view angles, even though the training images are diverse and unaligned, gathered from “in-the-wild” real-world environments.
Generated 128×128 samples on ImageNet.
Generated 360° samples on ImageNet.
Generated 256×256 samples on ImageNet.
Generated 128×128 samples on SDIP Dogs, SDIP Elephants, and LSUN Horses.

The key idea of our method is to formulate the 3D-aware image generation task as multiview 2D image set generation. Our assumption is that the distribution of 3D assets, is equivalent to the joint distribution of its corresponding multiview images. This assumption is derived from the bijective correspondence between 3D assets and their multiview projections, given a sufficient number of views. To generate a set of multiview images follow their joint distribution, we then factorized it into the multiplication of an unconditional distribution and a series of conditional distributions with the chain rule of probability.

\[ \begin{split} q_a(\mathbf x)=\ &q_i(\Gamma(\mathbf x, \boldsymbol\pi_0))\cdot\\ &q_i(\Gamma(\mathbf x, \boldsymbol\pi_1)|\Gamma(\mathbf x, \boldsymbol\pi_0))\cdot\\ &\cdots\\ &q_i(\Gamma(\mathbf x, \boldsymbol\pi_N)|\Gamma(\mathbf x, \boldsymbol\pi_0),\cdots,\Gamma(\mathbf x, \boldsymbol\pi_{N-1})) \end{split} \]

In practice, however, multiview images are also difficult to obtain. To use unstructured 2D image collections, we construct training data using depth-based image warping. Then, two diffusion models are trained to fit the unconditional and conditional distributions, respectively.

Our method contains two diffusion models \(\mathcal{G}_u\) and \(\mathcal{G}_c\). \(\mathcal{G}_u\) is an unconditional model for randomly generating the first view, and \(\mathcal{G}_c\) is a conditional generator for novel views. With aggregated conditioning, multiview images are obtained iteratively by refining and completing previously synthesized views.


@inproceedings{xiang2023ivid, title = {3D-aware Image Generation using 2D Diffusion Models}, author = {Xiang, Jianfeng and Yang, Jiaolong and Huang, Binbin and Tong, Xin}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {2383-2393} }

3D-aware Image Generation using 2D Diffusion Models
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