  Recent text-to-image generative models have exhibited remarkable abilities in
generating high-fidelity and photo-realistic images. However, despite the
visually impressive results, these models often struggle to preserve plausible
human structure in the generations. Due to this reason, while generative models
have shown promising results in aiding downstream image recognition tasks by
generating large volumes of synthetic data, they are not suitable for improving
downstream human pose perception and understanding. In this work, we propose a
Diffusion model with Human Pose Correction (Diffusion-HPC), a text-conditioned
method that generates photo-realistic images with plausible posed humans by
injecting prior knowledge about human body structure. Our generated images are
accompanied by 3D meshes that serve as ground truths for improving Human Mesh
Recovery tasks, where a shortage of 3D training data has long been an issue.
Furthermore, we show that Diffusion-HPC effectively improves the realism of
human generations under varying conditioning strategies.
