StyleDiT：基于风格潜在扩散变换器的多样儿童与伴侣人脸合成统一框架
StyleDiT: A Unified Framework for Diverse Child and Partner Faces Synthesis with Style Latent Diffusion Transformer
AI总结：
针对亲属人脸合成中多样性与保真度难以兼顾的问题，提出融合StyleGAN与扩散模型的StyleDiT框架，并引入关系特征引导机制，实现高质量多样化亲属及伴侣人脸合成。
摘要：
亲属人脸合成是一个具有挑战性的问题，因为可用的亲属数据稀缺且质量较低。现有方法通常难以在精确控制年龄和性别等面部属性的同时，生成既具有高多样性又具有高保真度的后代。为解决这些问题，我们提出了风格潜在扩散变换器（StyleDiT），这是一个新颖的框架，将StyleGAN的优势与扩散模型相结合，以生成高质量且多样化的亲属人脸。在该框架中，StyleGAN丰富的面部先验实现了细粒度的属性控制，而我们的条件扩散模型利用建模复杂亲属关系分布的优势，用于采样与条件图像的亲属关系对齐的StyleGAN潜在变量。随后，StyleGAN处理潜在变量解码以生成最终人脸。此外，我们引入了关系特征引导（RTG）机制，使得能够独立控制影响条件，例如每位父母的面部图像。RTG还能够在合成人脸中实现多样性与保真度之间的细粒度调整。进一步地，我们将应用扩展到未探索的领域：在同一框架内，使用儿童图像和一位父母的图像预测伴侣的面部图像。大量实验表明，我们的StyleDiT在生成多样且高保真的亲属人脸方面取得了出色的平衡，优于现有方法。
英文摘要：
Kinship face synthesis is a challenging problem due to the scarcity and low quality of the available kinship data. Existing methods often struggle to generate descendants with both high diversity and fidelity while precisely controlling facial attributes such as age and gender. To address these issues, we propose the Style Latent Diffusion Transformer (StyleDiT), a novel framework that integrates the strengths of StyleGAN with the diffusion model to generate high-quality and diverse kinship faces. In this framework, the rich facial priors of StyleGAN enable fine-grained attribute control, while our conditional diffusion model is used to sample a StyleGAN latent aligned with the kinship relationship of conditioning images by utilizing the advantage of modeling complex kinship relationship distribution. StyleGAN then handles latent decoding for final face generation. Additionally, we introduce the Relational Trait Guidance (RTG) mechanism, enabling independent control of influencing conditions, such as each parent's facial image. RTG also enables a fine-grained adjustment between the diversity and fidelity in synthesized faces. Furthermore, we extend the application to an unexplored domain: predicting a partner's facial images using a child's image and one parent's image within the same framework. Extensive experiments demonstrate that our StyleDiT outperforms existing methods by striking an excellent balance between generating diverse and high-fidelity kinship faces.
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。
