UniBioTransfer: A Unified Framework for Multiple Biometrics Transfer

DeepFace generation has traditionally followed a task-driven paradigm. However, such a single-task setting severely limits model generalization and scalability. A unified model capable of solving multiple deepface generation tasks in a single pass represents a promising and practical future direction, but remains extremely challenging due to data scarcity and serious cross-task conflicts arising from heterogeneous attributes transformations. To this end, we propose UniBioTransfer, the first unified framework capable of handling both conventional deepface tasks, e.g., face transfer and face reenactment, and tasks with huge shape-varying transformations, e.g., hair transfer and head transfer. Far more beyond, UniBioTransfer naturally generalizes to unseen tasks, like lip, eye, and glasses transfer, with minimal model fine-tuning. In simple words, UniBioTransfer achieves the goal by addressing data insufficiency challenge in multi-task generation through a unified data construction strategy that can handle diverse and distinct attributes, and mitigates cross-task interference via an innovative BioMoE, a Mixture-of-Experts based model coupled with a novel two-stage training strategy that effectively disentangles task-specific knowledge. Extensive experiments demonstrate the effectiveness, and generalization, and scalability of UniBioTransfer, outperforming both existing multi-task models and task-specific methods across a wide range of tasks, while maintaining a comparable model size and training time. We will release our code upon acceptance.