3D-aware image generation necessitates extensive training data to ensure stable training and mitigate the risk of overfitting. This paper first consider a novel task known as One-shot 3D Generative Domain Adaptation (GDA), aimed at transferring a pre-trained 3D generator from one domain to a new one, relying solely on a single reference image. One-shot 3D GDA is characterized by the pursuit of specific attributes, namely, high fidelity, large diversity, cross-domain consistency, and multi-view consistency. Within this paper, we introduce 3D-Adapter, the first one-shot 3D GDA method, for diverse and faithful generation. Our approach begins by judiciously selecting a restricted weight set for fine-tuning, and subsequently leverages four advanced loss functions to facilitate adaptation. An efficient progressive fine-tuning strategy is also implemented to enhance the adaptation process. The synergy of these three technological components empowers 3D-Adapter to achieve remarkable performance, substantiated both quantitatively and qualitatively, across all desired properties of 3D GDA. Furthermore, 3D-Adapter seamlessly extends its capabilities to zero-shot scenarios, and preserves the potential for crucial tasks such as interpolation, reconstruction, and editing within the latent space of the pre-trained generator. Code will be available at https://github.com/iceli1007/3D-Adapter.

3D 感知图像生成需要大量的训练数据，以确保稳定的训练并降低过拟合的风险。本文首先考虑了一项称为 One-shot 3D 生成域适应 （GDA） 的新任务，旨在仅依靠单个参考图像将预先训练的 3D 生成器从一个域转移到新域。One-shot 3D GDA 的特点是追求特定属性，即高保真、大多样性、跨域一致性和多视图一致性。在本文中，我们介绍了 3D-Adapter，这是第一个一次性 3D GDA 方法，用于多样化和忠实的生成。我们的方法首先明智地选择一个受限的权重集进行微调，然后利用四个高级损失函数来促进适应。还实施了一种有效的渐进式微调策略来增强适应过程。这三个技术组件的协同作用使 3D-Adapter 能够在 3D GDA 的所有所需特性上实现卓越的性能，这在定量和定性方面都得到了证实。此外，3D-Adapter 将其功能无缝扩展到零镜头场景，并在预训练生成器的潜在空间内保留了插值、重建和编辑等关键任务的潜力。代码将在 https://github.com/iceli1007/3D-Adapter 上提供。