Abstract
There has been significant progress in human image generation in recent years, particularly with the introduction of diffusion models. However, it is challenging for the existing methods to produce consistent hand anatomy, and the generated images often lack precise control over hand pose. To address this limitation, we introduce a novel two-stage approach to pose-conditioned human image generation. Firstly, we generate detailed hands and then outpaint the body around those hands. We propose training the hand generator in a multi-task setting to produce both hand image and their corresponding segmentation masks, and employ the trained model in the first stage of generation. An adapted ControlNet model is then used in the second stage to outpaint the body. We introduce a novel blending technique that combines the results of both stages in a coherent way and preserves the hand details. It involves sequential expansion of the outpainted region while fusing the latent representations, to ensure a seamless and cohesive synthesis of the final image. Experimental evaluations demonstrate the superiority of our proposed method over state-of-the-art techniques in both pose accuracy and image quality, as validated on the HaGRID and YouTube-ASL datasets. Our approach not only enhances the quality of the generated hands, but also offers improved control over hand pose, advancing the capabilities of pose-conditioned human image generation. We make the code available.