UniBioTransfer: A Unified Framework for Multiple Biometrics Transfer

TL;DR

UniBioTransfer introduces a versatile, unified deepface generation framework that handles multiple tasks and generalizes to unseen transformations, overcoming data scarcity and cross-task conflicts with innovative strategies.

Contribution

It is the first unified model capable of multi-task deepface generation, addressing data insufficiency and cross-task interference through novel data construction and model strategies.

Findings

Outperforms existing models across various deepface tasks.

Effectively generalizes to unseen face attribute transfers.

Demonstrates scalability and robustness in diverse scenarios.

Abstract

Deepface generation has traditionally followed a task-driven paradigm, where distinct tasks (e.g., face transfer and hair transfer) are addressed by task-specific models. Nevertheless, this 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 direction, yet remains challenging due to data scarcity and cross-task conflicts arising from heterogeneous attribute 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 shape-varying transformations (e.g., hair transfer and head transfer). Besides, UniBioTransfer naturally generalizes to unseen tasks, like lip, eye, and glasses transfer, with minimal fine-tuning. Generally, UniBioTransfer addresses data insufficiency in multi-task generation through a unified data construction strategy, including a swapping-based corruption mechanism designed for spatially dynamic attributes like hair. It further 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, generalization, and scalability of UniBioTransfer, outperforming both existing unified models and task-specific methods across a wide range of deepface generation tasks. Project page is at https://scy639.github.io/UniBioTransfer.github.io/