Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
Authors: Renshuai Liu, Bowen Ma, Wei Zhang, Zhipeng Hu, Changjie Fan, Tangjie Lv, Yu Ding, Xuan Cheng

In human-centric content generation the pre-trained text-to-image models struggle to produce user-wanted portrait images which retain the identity of individuals while exhibiting diverse expressions. This paper introduces our efforts towards personalized face generation. To this end we propose a novel multi-modal face generation framework capable of simultaneous identity-expression control and more fine-grained expression synthesis. Our expression control is so sophisticated that it can be specialized by the fine-grained emotional vocabulary. We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment. Due to the entanglement of identity and expression separately and precisely controlling them within one framework is a nontrivial task thus has not been explored yet. To overcome this we propose several innovative designs in the conditional diffusion model including balancing identity and expression encoder improved midpoint sampling and explicitly background conditioning. Extensive experiments have demonstrated the controllability and scalability of the proposed framework in comparison with state-of-the-art text-to-image face swapping and face reenactment methods.