一个统一框架，能一键完成人脸、头发、头部等多种形象迁移。
UniBioTransfer: A Unified Framework for Multiple Biometrics Transfer
- 用统一模型处理多种人脸生成任务，避免为每项任务单独建模。
- 在多个任务上超越现有方法，支持未见任务仅微调即可适配。
- 通过动态数据构造和专家混合机制，解决数据少与任务冲突问题。
深度人脸生成长期依赖特定任务的模型，难以泛化。本文提出UniBioTransfer，首个可单次处理多种深度人脸生成任务（如人脸迁移、表情重演、头发迁移、头型迁移）的统一框架。该框架通过基于交换的动态数据构造策略缓解数据不足问题，尤其适用于头发等空间动态属性；并引入BioMoE（基于专家混合的模型）与两阶段训练，有效解耦不同任务知识，减少干扰。实验表明，它在广泛任务中优于现有统一模型与专用模型，且能对唇部、眼部、眼镜等未见任务实现零样本迁移，仅需少量微调即可适配。项目主页：https://scy639.github.io/UniBioTransfer.github.io/
原文摘要 · Abstract (English)
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/
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