基于语义潜在扩散模型的可控人脸合成
Controllable Face Synthesis with Semantic Latent Diffusion Models
AI总结：
本文提出一种基于新型潜在扩散模型架构的语义图像合成框架，利用SPADE归一化和交叉注意力层融合形状与风格信息，实现对人脸各语义部分的精确控制，并在质量和多样性上超越现有最先进方法。
摘要：
语义图像合成（SIS）是人脸生成与编辑领域中最受欢迎且最有效的技术之一，这得益于其良好的生成质量及其带来的多功能性。近期的研究试图超越标准的基于GAN的框架，并开始探索用于该任务的扩散模型（DMs），因为它们在质量和多样性方面均优于GAN。另一方面，DMs缺乏细粒度的可控性和可重复性。为了解决这一问题，本文提出了一种基于新型潜在扩散模型（Latent Diffusion Model）架构的SIS框架，用于人脸生成与编辑，该框架既能重现和操纵真实参考图像，又能生成由多样性驱动的结果。所提出的系统利用SPADE归一化和交叉注意力层来融合形状与风格信息，从而允许对人脸的每个语义部分进行精确控制。这是先前的最先进方法无法实现的。最后，我们进行了大量实验，从定性和定量两方面证明了我们的模型超越了当前的最先进水平。
英文摘要：
Semantic Image Synthesis (SIS) is among the most popular and effective techniques in the field of face generation and editing, thanks to its good generation quality and the versatility is brings along. Recent works attempted to go beyond the standard GAN-based framework, and started to explore Diffusion Models (DMs) for this task as these stand out with respect to GANs in terms of both quality and diversity. On the other hand, DMs lack in fine-grained controllability and reproducibility. To address that, in this paper we propose a SIS framework based on a novel Latent Diffusion Model architecture for human face generation and editing that is both able to reproduce and manipulate a real reference image and generate diversity-driven results. The proposed system utilizes both SPADE normalization and cross-attention layers to merge shape and style information and, by doing so, allows for a precise control over each of the semantic parts of the human face. This was not possible with previous methods in the state of the art. Finally, we performed an extensive set of experiments to prove that our model surpasses current state of the art, both qualitatively and quantitatively.
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。
