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.

In this paper, we propose a novel optimization framework to synthesize an aesthetic pose for the virtual character with respect to the presented user's pose. Our approach applies aesthetic evaluation that exploits fully connected neural networks trained on example images. The aesthetic pose of the virtual character is obtained by optimizing a cost function that guides the rotation of each body joint angles. In our experiments, we demonstrate the proposed approach can synthesize poses for virtual characters according to user pose inputs. We also conducted objective and subjective experiments of the synthesized results to validate the efficacy of our approach.