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arXiv · 2603.00159

FlowPortrait: Reinforcement Learning for Audio-Driven Portrait Video Generation

Weiting Tan·Andy T. Liu·Ming Tu·Xinghua Qu·Philipp Koehn·Lu Lu
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Abstract

Generating realistic talking-head videos remains challenging due to persistent issues such as imperfect lip synchronization, unnatural motion, and evaluation metrics that correlate poorly with human perception. We propose FlowPortrait, a reinforcement-learning framework for audio-driven portrait animation built on a multimodal backbone for autoregressive audio-to-video generation. FlowPortrait introduces a human-aligned evaluation system based on Multimodal Large Language Models (MLLMs) to assess lip-sync accuracy, expressiveness, and motion quality. These signals are combined with perceptual and temporal consistency regularizers to form a stable composite reward, which is used to post-train the generator via Group Relative Policy Optimization (GRPO). Extensive experiments, including both automatic evaluations and human preference studies, demonstrate that FlowPortrait consistently produces higher-quality talking-head videos, highlighting the effectiveness of reinforcement learning for portrait animation.

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Weiting Tan, Andy T. Liu, Ming Tu, Xinghua Qu, Philipp Koehn, Lu Lu. 2026-02-25. FlowPortrait: Reinforcement Learning for Audio-Driven Portrait Video Generation. https://arxiv.org/abs/2603.00159

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