15 Citations (Scopus)


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. Code is available at https://github.com/ankanbhunia/PIDM.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
Number of pages9
ISBN (Electronic)979-8-3503-0129-8
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventIEEE Conference on Computer Vision and Pattern Recognition - Vancouver, Canada
Duration: 18 Jun 202322 Jun 2023

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


ConferenceIEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR


  • Image and video synthesis and generation


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