用生成模型精准编辑猕猴面部性别，实现真实且可量化的图像调控。
Generation and Editing of Mandrill Faces: Application to Sex Editing and Assessment
- 在特定GAN的隐空间中定位性别轴，实现雄雌猕猴脸像的可控生成与编辑。
- 生成图像在真实感和性别准确性上表现优异，符合野外行为实验需求。
- 提出基于真实图像统计特征的量化评估方法，突破传统主观判断局限。
近年来，生成式AI在提升合成图像（计算机生成图像）真实性方面取得显著进展，同时也实现了对图像特定特征的编辑。以往研究基于生成对抗网络（GAN）生成逼真人脸，并修改特定属性，但尚未应用于特定动物物种。此外，结果评估多依赖主观判断，缺乏量化标准。本文提出一种基于GAN的方法，用于生成雄性或雌性猕猴的面部图像。其主要创新在于通过识别特定GAN隐空间中的性别轴，实现猕猴面部性别的可控编辑。同时，我们基于真实图像分布提取的统计特征，建立了性别水平的量化评估体系。实验结果表明，所生成图像不仅具有高度真实性，且性别准确性达标，满足未来针对野生猕猴的行为学实验需求。
原文摘要 · Abstract (English)
Generative AI has seen major developments in recent years, enhancing the realism of synthetic images, also known as computer-generated images. In addition, generative AI has also made it possible to modify specific image characteristics through image editing. Previous work has developed methods based on generative adversarial networks (GAN) for generating realistic images, in particular faces, but also to modify specific features. However, this work has never been applied to specific animal species. Moreover, the assessment of the results has been generally done subjectively, rather than quantitatively. In this paper, we propose an approach based on methods for generating images of faces of male or female mandrills, a non-human primate. The main novelty of proposed method is the ability to edit their sex by identifying a sex axis in the latent space of a specific GAN. In addition, we have developed an assessment of the sex levels based on statistical features extracted from real image distributions. The experimental results we obtained from a specific database are not only realistic, but also accurate, meeting a need for future work in behavioral experiments with wild mandrills.
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