The increased demand for 3D data in AR/VR, robotics and gaming applications, gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these models rely on the Score Distillation Sampling (SDS) algorithm to optimize a 3D representation such that the rendered image maintains a high likelihood as evaluated by a pre-trained diffusion model. However, finding a correct mode in the high-dimensional distribution produced by the diffusion model is challenging and often leads to issues such as over-saturation, over-smoothing, and Janus-like artifacts. In this paper, we propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Instead of focusing on mode-seeking, our method directly models the distribution discrepancy between multi-view renderings and diffusion priors in an adversarial manner, which unlocks the generation of high-fidelity and photorealistic 3D content, conditioned on a single image and prompt. Moreover, by harnessing the latent space of GANs and expressive diffusion model priors, our method facilitates a wide variety of 3D applications including single-view reconstruction, high diversity generation and continuous 3D interpolation in the open domain. The experiments demonstrate the superiority of our pipeline compared to previous works in terms of generation quality and diversity.

利用预训练扩散模型的一种新型学习方法，直接在对抗性的方式下对多视图渲染与扩散先验之间的分布差异进行建模，从而实现了高保真度和逼真度的三维内容生成，条件为单张图像和提示。通过利用生成对抗网络（GANs）的潜在空间和表达力丰富的扩散模型先验，我们的方法促进了各种三维应用，包括单视图重建、高多样性生成和开放域中的连续三维插值。实验结果表明，与以往的工作相比，我们的流程在生成质量和多样性方面表现出更强的优势。

基于对抗蒸馏的计算机辅助设计：逼真三维生成