用生成模型模拟人脸变化，揭示外部因素对身份形成的影
Leveraging Generative AI Models to Explore Human Identity
- 通过扩散模型生成人脸图像，建立生成过程与身份形成之间的类比
- 输入变化导致生成人脸显著改变，间接证明身份依赖外部因素
- 创作互动视频艺术作品，直观呈现身份的流动性和可塑性
本文通过间接方式利用神经网络探索人类身份。为此，采用最先进的生成式人工智能模型——扩散模型，该模型可训练生成人脸图像。通过将生成的人脸与人类身份关联，建立了扩散模型生成人脸的过程与人类身份形成过程之间的对应关系。实验表明，扩散模型外部输入的变化会引发生成人脸图像的显著变化。基于这一对应关系，我们间接证实了在身份形成过程中，人类身份对外部因素具有依赖性。此外，本文还推出了名为《身份的流动性》的视频艺术作品，以表达在不同外部因素影响下身份的动态变化特征，视频可访问 https://www.behance.net/gallery/219958453/Fluidity-of-Human-Identity。
原文摘要 · Abstract (English)
This paper attempts to explore human identity by utilizing neural networks in an indirect manner. For this exploration, we adopt diffusion models, state-of-the-art AI generative models trained to create human face images. By relating the generated human face to human identity, we establish a correspondence between the face image generation process of the diffusion model and the process of human identity formation. Through experiments with the diffusion model, we observe that changes in its external input result in significant changes in the generated face image. Based on the correspondence, we indirectly confirm the dependence of human identity on external factors in the process of human identity formation. Furthermore, we introduce \textit{Fluidity of Human Identity}, a video artwork that expresses the fluid nature of human identity affected by varying external factors. The video is available at https://www.behance.net/gallery/219958453/Fluidity-of-Human-Identity?.
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