Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Progressive Conditional Diffusion Models (PCDMs) that incrementally bridge the gap between person images under the target and source poses through three stages. Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance. Then, the second stage establishes a dense correspondence between the source and target images using the global features from the previous stage, and an inpainting conditional diffusion model is proposed to further align and enhance the contextual features, generating a coarse-grained person image. In the third stage, we propose a refining conditional diffusion model to utilize the coarsely generated image from the previous stage as a condition, achieving texture restoration and enhancing fine-detail consistency. The three-stage PCDMs work progressively to generate the final high-quality and high-fidelity synthesized image. Both qualitative and quantitative results demonstrate the consistency and photorealism of our proposed PCDMs under challenging scenarios.The code and model will be available at https://github.com/muzishen/PCDMs.

翻译：近期研究展示了扩散模型在姿态引导人物图像合成中的显著潜力。然而，由于源图像与目标图像之间的姿态不一致，仅依赖源图像和目标姿态信息合成具有不同姿态的图像仍是一项艰巨挑战。本文提出渐进式条件扩散模型（PCDMs），通过三个阶段逐步弥合目标姿态与源姿态下人物图像之间的差距。具体而言：第一阶段，我们设计了一个简单的先验条件扩散模型，通过挖掘姿态坐标与图像外观之间的全局对齐关系来预测目标图像的全局特征；第二阶段，利用前一阶段的全局特征建立源图像与目标图像之间的密集对应关系，并提出一种补全条件扩散模型进一步对齐和增强上下文特征，生成粗粒度的人物图像；第三阶段，提出精炼条件扩散模型，将前一阶段粗生成的图像作为条件输入，实现纹理恢复并增强细粒度一致性。三个阶段的PCDMs渐进式协作，最终生成高质量、高保真的合成图像。定性与定量结果均证明了所提PCDMs在挑战性场景下的一致性及逼真度。相关代码与模型将开源至https://github.com/muzishen/PCDMs。