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arXiv · 2606.22347

Customizing Video Portraits via Identity-ActionDecoupling

Junxiong Lin·Haoran Wang·Xinji Mai·Zeng Tao·Xuan Tong·Ivy Pan·Wenqiang Zhang
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Abstract

Identity-Preserving Text-to-Video Generation (IPT2V) seeks to synthesize a temporally coherent video from a reference image and a textual description, while simultaneously preserving the subject's identity and allowing fine-grained control over facial dynamics. Although recent methods such as ID-Animator and ConsisID inject identity features only at inference time, they ignored the ID-irrelevant information contained in Facial embedding, leading to monotonous or inaccurate facial movements that poorly follow the prompt. We introduce Identity-Action Decoupling (IaD) framework as well as two loss function Identity Decoupling Loss and Text Alignment Loss to solve this problem. Without any subject-specific fine-tuning, IaD yields videos that (1) maintain cross-temporal identity consistency and (2) exhibit rich, controllable expressions and scene variations that closely match the input text.

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Junxiong Lin, Haoran Wang, Xinji Mai, Zeng Tao, Xuan Tong, Ivy Pan, Wenqiang Zhang. 2026-06-21. Customizing Video Portraits via Identity-ActionDecoupling. https://arxiv.org/abs/2606.22347

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