HyperEditor：通过超网络在图像编辑中同时实现真实性与跨域能力
HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks
AI总结：
提出HyperEditor，利用超网络生成权重因子重分配StyleGAN2生成器权重，结合CLIP语义对齐，实现真实属性编辑与跨域风格迁移，并引入自适应层选择器提升效率。
摘要：
在真实图像编辑中实现真实性同时进行跨域编辑仍然是一个挑战。近期研究侧重于将真实图像转换为潜在编码，并通过操作这些编码来完成图像编辑。然而，仅操作潜在编码会将编辑后的图像限制在生成器的图像域内，阻碍了实现多样化编辑目标。为此，我们提出了一种创新的图像编辑方法，称为HyperEditor，它利用超网络生成的权重因子来重新分配预训练StyleGAN2生成器的权重。在CLIP的跨模态图像-文本语义对齐的引导下，这种创新方法使我们能够同时完成真实的属性编辑和跨域风格迁移，这是以往方法未能实现的能力。此外，我们确认仅修改生成器中特定层的权重即可产生等效的编辑结果。因此，我们引入了一个自适应层选择器，使我们的超网络能够自主识别需要输出权重因子的层，这可以进一步提高我们超网络的效率。在大量具有挑战性的数据集上进行的大量实验证明了我们方法的有效性。
英文摘要：
Editing real images authentically while also achieving cross-domain editing remains a challenge. Recent studies have focused on converting real images into latent codes and accomplishing image editing by manipulating these codes. However, merely manipulating the latent codes would constrain the edited images to the generator's image domain, hindering the attainment of diverse editing goals. In response, we propose an innovative image editing method called HyperEditor, which utilizes weight factors generated by hypernetworks to reassign the weights of the pre-trained StyleGAN2's generator. Guided by CLIP's cross-modal image-text semantic alignment, this innovative approach enables us to simultaneously accomplish authentic attribute editing and cross-domain style transfer, a capability not realized in previous methods. Additionally, we ascertain that modifying only the weights of specific layers in the generator can yield an equivalent editing result. Therefore, we introduce an adaptive layer selector, enabling our hypernetworks to autonomously identify the layers requiring output weight factors, which can further improve our hypernetworks' efficiency. Extensive experiments on abundant challenging datasets demonstrate the effectiveness of our method.
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。
