Climaxing VR character with scene-aware aesthetic dress synthesis

Like real humans, virtual characters also need to dress up according to different application scenarios so that the virtual character appears professionally, harmoniously, and naturally. However, manual selection is tedious, and the appearances of virtual characters usually lack variety. In this paper, we propose a new problem of synthesizing appropriate dress for a virtual character based on the scenario analysis where he/she shows up. We come up with a pipeline to tackle the scenario-aware dress synthesis problem. Firstly, given a scene, our approach predicts a dress code from the extracted high-level information in the scene, consisting of season, occasion, and scene category. Then our approach tunes the dress details to fit the aesthetic criteria and the virtual character's attributes. An optimization of a cost function implements the tuning process. We carried out experiments to validate the efficacy of the proposed approach. The perceptual study results show the good performance of our approach.