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.

翻译：扩散模型已成为二维图像生成建模的最优方法。其成功部分归功于能够通过稳定的学习目标在数百万甚至数十亿张图像上进行训练。然而，将这些模型扩展到三维仍面临两大困难。首先，获取大规模三维训练数据比二维图像复杂得多。其次，虽然概念上可将模型从二维网格扩展至三维，但由此产生的内存与计算复杂度的立方级增长使其难以实现。针对第一个挑战，我们提出一种新型扩散框架，该框架仅需带位姿信息的二维图像作为监督即可进行端到端训练；针对第二个挑战，我们设计了一种图像形成模型，将模型内存与空间内存解耦。我们在CO3D数据集（此前未被用于训练三维生成模型）等真实数据上评估了该方法。实验表明，我们的扩散模型具有可扩展性、训练稳定性，并且在样本质量与保真度方面与现有三维生成建模方法具有竞争力。