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ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation

2023-05-24 05:28:37
Dongxu Yue, Qin Guo, Munan Ning, Jiaxi Cui, Yuesheng Zhu, Li Yuan

Abstract

Editing real facial images is a crucial task in computer vision with significant demand in various real-world applications. While GAN-based methods have showed potential in manipulating images especially when combined with CLIP, these methods are limited in their ability to reconstruct real images due to challenging GAN inversion capability. Despite the successful image reconstruction achieved by diffusion-based methods, there are still challenges in effectively manipulating fine-gained facial attributes with textual this http URL address these issues and facilitate convenient manipulation of real facial images, we propose a novel approach that conduct text-driven image editing in the semantic latent space of diffusion model. By aligning the temporal feature of the diffusion model with the semantic condition at generative process, we introduce a stable manipulation strategy, which perform precise zero-shot manipulation effectively. Furthermore, we develop an interactive system named ChatFace, which combines the zero-shot reasoning ability of large language models to perform efficient manipulations in diffusion semantic latent space. This system enables users to perform complex multi-attribute manipulations through dialogue, opening up new possibilities for interactive image editing. Extensive experiments confirmed that our approach outperforms previous methods and enables precise editing of real facial images, making it a promising candidate for real-world applications. Project page: this https URL

Abstract (translated)

编辑真实的面部图像是计算机视觉中一个重要的任务,在各种实际应用场景中具有巨大的需求。尽管基于GAN的方法在操纵图像方面表现出了潜力,特别是在结合Clip时更是如此,但这些方法在重建真实图像的能力上受到挑战,因为GAN的逆运算能力具有挑战性。尽管扩散方法成功地重建了图像,但仍然存在有效地操纵微调的面部属性通过文本这个http URL解决这些问题并方便真实面部图像操纵的需求,因此我们提出了一种 novel 的方法,在扩散模型的语义潜在空间中进行文本驱动的图像编辑。通过将扩散模型的时间特性与生成过程中语义条件对齐,我们引入了一种稳定的操纵策略,可以实现精确的零次操作操纵。此外,我们开发了一个名为ChatFace的交互系统,它结合了大型语言模型的零次操作推理能力,在扩散语义潜在空间中高效地进行操纵。该系统使用户通过对话进行复杂的多属性操纵,打开了交互图像编辑的新可能性。广泛的实验确认了我们的 approach 比先前的方法更有效,使用户可以精确地编辑真实的面部图像,使其成为实际应用场景中的有前途的选择。项目页面: this https URL

URL

https://arxiv.org/abs/2305.14742

PDF

https://arxiv.org/pdf/2305.14742.pdf


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