The traditional controllable face generation refers to the controllability of coarse-grained ranges such as facial features, expression postures, or viewing angles, but specific application scenarios require finer-grained control. This paper proposes a fine-grained and controllable face generation technology, CFTF. CFTF allows users to participate deeply in the face generation process through multiple rounds of language feedback. It not only enables control over coarse-grained features such as gender and viewing angle, but also provides flexible control over details such as hair color, accessories, and iris color. We apply CFTF to the suspect portrait scene, and perform multiple rounds of human-computer interaction based on the eyewitness's painting sketch of the suspect and descriptions of their facial features, realizing the "Human-in-the-loop" collaborative portrait drawing.

Virtual standard patient (VSP) is in high demand for medical students' diagnosis ability training in an efficient manner. Different from the traditional conversation system in medical dialogue generation, VSP needs a novel conversation paradigm to act as the patient instead of the doctor. However, existing conversation techniques still have limited ability in terms of generation of symptoms exhibited by patients with the personalized and knowledge-centered expressions. To alleviate these problems, we propose to construct a novel oral knowledge graph, which sufficiently provides medical clues of the certain disease. Accordingly, the VSP could accurately interact with the dentists for their underlying intention and express the symptoms characters in a natural style. To efficiently retrieve the related disease clues, the symptoms descriptions of the oral diseases are encoded into the oral knowledge graph, which could well organize the disease-centered symptom entities and speaking styles. Moreover, to transfer the common sense knowledge from existing large scale of medical knowledge graph to the specific oral knowledge graph, a coupled pre-trained Bert models is further designed to learn the related medical knowledge from coarse-level to fine-level hierarchically. Finally, a series of well-designed personalized templates are proposed to generate plausible and realistic answers in condition of the certain disease. We also conduct extensive user studies to demonstrate that the VSP satisfies the medical students' diagnosis practice requirement in terms of naturalness, realism, and topic relevance.