Paste You Into Game: Towards Expression and Identity Consistency Face Swapping
Hao Zeng, Wei Zhang, Keyu Chen, Zhimeng Zhang, Lincheng Li, Yu Ding · 2022
Customizing game characters for individual players has been a long-standing attractive feature in the game industry. However, traditional solutions like manual editing within a game engine are always time-consuming and unsatisfying. Our work proposes a novel automatic face swapping method for arbitrary users and game characters, addressing three challenges including style gap between human and game faces, identity preservation, and expression consistency. A game face dataset is collected to handle the cross-style gap; an identity compound embedding is proposed to ease the bias existing in the commonly-used ID identifiers and it provides a more robust identity representation; a novel expression embedding loss is proposed to enforce the expression consistency between the swapped and target faces and it achieves better expression consistency than the previous methods, especially when the expression is very subtle. The visualized results, as well as the qualitative and quantitative comparisons, reveal the significance and effectiveness of our proposed solutions.