Diffusion models have emerged as the best approach for generative modeling of 2D images. Part of their success is due to the possibility of training them on millions if not billions of images with a stable learning objective. However, extending these models to 3D remains difficult for two reasons. First, finding a large quantity of 3D training data is much more complex than for 2D images. Second, while it is conceptually trivial to extend the models to operate on 3D rather than 2D grids, the associated cubic growth in memory and compute complexity makes this infeasible. We address the first challenge by introducing a new diffusion setup that can be trained, end-to-end, with only posed 2D images for supervision; and the second challenge by proposing an image formation model that decouples model memory from spatial memory. We evaluate our method on real-world data, using the CO3D dataset which has not been used to train 3D generative models before. We show that our diffusion models are scalable, train robustly, and are competitive in terms of sample quality and fidelity to existing approaches for 3D generative modeling.

本研究提出了一种新的扩散设置，可以仅通过2D图像监督就可以进行端到端的训练，同时提出的图像形成模型将模型内存与空间内存分离，以应对3D扩散模型在训练上遇到的问题，并在CO3D数据集上进行的实验表明，该方法是可扩展的，具有稳健性，与现有的3D生成建模方法在样本质量和保真度方面相当竞争。

HOLODIFFUSION：使用2D图像训练3D扩散模型