  Pose-guided person image synthesis task requires re-rendering a reference
image, which should have a photorealistic appearance and flawless pose
transfer. Since person images are highly structured, existing approaches
require dense connections for complex deformations and occlusions because these
are generally handled through multi-level warping and masking in latent space.
The feature maps generated by convolutional neural networks do not have
equivariance, and hence multi-level warping is required to perform pose
alignment. Inspired by the ability of the diffusion model to generate
photorealistic images from the given conditional guidance, we propose recurrent
pose alignment to provide pose-aligned texture features as conditional
guidance. Due to the leakage of the source pose in conditional guidance, we
propose gradient guidance from pose interaction fields, which output the
distance from the valid pose manifold given a predicted pose as input. This
helps in learning plausible pose transfer trajectories that result in
photorealism and undistorted texture details. Extensive results on two
large-scale benchmarks and a user study demonstrate the ability of our proposed
approach to generate photorealistic pose transfer under challenging scenarios.
Additionally, we demonstrate the efficiency of gradient guidance in pose-guided
image generation on the HumanArt dataset with fine-tuned stable diffusion.
