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Diffusion models when conditioned on text prompts, generate realistic-lookingimages with intricate details. But most of these pre-trained models fail togenerate accurate images when it comes to human features like hands, teeth,etc. We hypothesize that this inability of diffusion models can be overcomethrough well-annotated good-quality data. In this paper, we look specificallyinto improving the hand-object-interaction image generation using diffusionmodels. We collect a well annotated hand-object interaction synthetic datasetcurated using Prompt-Propose-Verify framework and finetune a stable diffusionmodel on it. We evaluate the image-text dataset on qualitative and quantitativemetrics like CLIPScore, ImageReward, Fedility, and alignment and showconsiderably better performance over the current state-of-the-art benchmarks.

Prompt-Propose-Verify: A Reliable Hand-Object-Interaction Data Generation Framework using Foundational Models