局部控制的面部老化与潜在扩散模型
Locally Controlled Face Aging with Latent Diffusion Models
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中文总结 AI 辅助
本文提出利用潜在扩散模型实现局部控制的面部老化，通过细粒度控制提升生成结果的真实性和可控性。
中文摘要 AI 辅助
我们提出了一种新的面部老化方法，以解决当前方法将老化视为全球、同质过程的局限性。现有技术使用GANs和扩散模型时，通常将生成过程条件化为参考图像和目标年龄，忽略了由于内在时间因素和外在因素如日晒导致的面部区域异质性老化。我们的方法利用潜在扩散模型，通过局部老化迹象选择性地老化特定面部区域。该方法在生成过程中提供了更细粒度的控制，使生成结果更加真实和个性化。我们采用潜在扩散细化器无缝融合这些局部老化区域，确保全局一致且自然的合成。实验结果表明，我们的方法有效实现了面部老化三个关键标准：身份的鲁棒性保留、高质量和逼真的图像，以及自然可控的老化进程。
英文摘要
We present a novel approach to face aging that addresses the limitations of current methods which treat aging as a global, homogeneous process. Existing techniques using GANs and diffusion models often condition generation on a reference image and target age, neglecting that facial regions age heterogeneously due to both intrinsic chronological factors and extrinsic elements like sun exposure. Our method leverages latent diffusion models to selectively age specific facial regions using local aging signs. This approach provides significantly finer-grained control over the generation process, enabling more realistic and personalized aging. We employ a latent diffusion refiner to seamlessly blend these locally aged regions, ensuring a globally consistent and natural-looking synthesis. Experimental results demonstrate that our method effectively achieves three key criteria for successful face aging: robust identity preservation, high-fidelity and realistic imagery, and a natural, controllable aging progression.
发表机构
- L’Oréal AI Research（欧莱雅人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。
