The pose-guided person image generation task requires synthesizing
photorealistic images of humans in arbitrary poses. The existing approaches use
generative adversarial networks that do not necessarily maintain realistic
textures or need dense correspondences that struggle to handle complex
deformations and severe occlusions. In this work, we show how denoising
diffusion models can be applied for high-fidelity person image synthesis with
strong sample diversity and enhanced mode coverage of the learnt data
distribution. Our proposed Person Image Diffusion Model (PIDM) disintegrates
the complex transfer problem into a series of simpler forward-backward
denoising steps. This helps in learning plausible source-to-target
transformation trajectories that result in faithful textures and undistorted
appearance details. We introduce a 'texture diffusion module' based on
cross-attention to accurately model the correspondences between appearance and
pose information available in source and target images. Further, we propose
'disentangled classifier-free guidance' to ensure close resemblance between the
conditional inputs and the synthesized output in terms of both pose and
appearance information. Our extensive results on two large-scale benchmarks and
a user study demonstrate the photorealism of our proposed approach under
challenging scenarios. We also show how our generated images can help in
downstream tasks. Our code and models will be publicly released.

使用去噪扩散模型，我们提出了一种名为 PIDM 的人体图像扩散模型，解决了复杂的转换问题，并展示了在两个大型基准测试中的显着结果，以及如何在下游任务中使用生成的图像。