ChildDiffusion: Unlocking the Potential of Generative AI and Controllable Augmentations for Child Facial Data using Stable Diffusion and Large Language Models

arXiv:2406.11592v1 Announce Type: new
Abstract: In this research work we have proposed high-level ChildDiffusion framework capable of generating photorealistic child facial samples and further embedding several intelligent augmentations on child facial data using short text prompts, detailed textual guidance from LLMs, and further image to image transformation using text guidance control conditioning thus providing an opportunity to curate fully synthetic large scale child datasets. The framework is validated by rendering high-quality child faces representing ethnicity data, micro expressions, face pose …

abstract arxiv child cs.cv data diffusion embedding framework generative guidance intelligent language language models large language large language models photorealistic potential prompts research samples stable diffusion text textual type work