Diffusion-HPC: Synthetic Data Generation for Human Mesh Recovery in Challenging Domains

Understand

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.