种子到种子：扩散种子空间中的无配对图像翻译
Seed-to-Seed: Unpaired Image Translation in Diffusion Seed Space
AI总结：
本文提出一种结合GAN和扩散模型的无配对图像翻译方法，通过利用预训练扩散模型的种子空间语义信息，实现复杂汽车场景的结构保持翻译，优于现有方法。
摘要：
我们引入了种子到种子翻译（StS），一种结合GAN和扩散模型（DMs）的新型方法，用于无配对图像到图像翻译。我们的方法旨在实现复杂汽车场景的全局翻译，其中必须严格遵循源图像的结构和语义。我们证明了预训练扩散模型的倒置潜在空间（种子空间）中编码的语义信息可用于判别任务，并利用此信息进行图像到图像翻译。我们的方法基于CycleGAN训练了一个sts-GAN，即无配对种子到种子翻译模型。翻译后的种子用作扩散模型采样过程的起点，同时通过ControlNet确保结构保持。我们展示了该方法在复杂汽车场景结构保持翻译中的有效性，展示了优于现有基于GAN和扩散模型的方法的性能。除了在汽车场景翻译中推动最先进的技术，我们的方法还为利用预训练扩散模型种子空间中的语义信息进行有效图像编辑和操控提供了新视角。
英文摘要：
We introduce Seed-to-Seed Translation (StS), a novel approach that combines GANs and diffusion models (DMs) for unpaired Image-to-Image Translation. Our approach is aimed at global translations of complex automotive scenes, where close adherence to the structure and semantics of the source image is essential. We demonstrate that the semantic information encoded in the space of inverted latents (seeds) of a pretrained DM, dubbed as the seed-space, can be used for discriminative tasks, and leverage this information to perform image-to-image translation. Our method involves training an sts-GAN, an unpaired seed-to-seed translation model, based on CycleGAN. The translated seeds are used as the starting point for the DM's sampling process, while structure preservation is ensured using a ControlNet. We demonstrate the effectiveness of our approach for structure-preserving translation of complex automotive scenes, showcasing superior performance compared to existing GAN-based and diffusion-based methods. In addition to advancing the SoTA in automotive scene translations, our approach offers a fresh perspective on leveraging the semantic information encoded within the seed-space of pretrained DMs for effective image editing and manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
