摘要：生成既能保留视觉身份、又能保留人物特有表达行为的人物中心视频，仍然是一项基础性挑战。除了复现外观外，模型还必须复现能够体现人物如何随时间表达情绪的面部行为。然而，大多数最先进方法仅以单张参考图像作为生成条件，而该图像并不包含这些时间动态信息。因此，这些方法往往能够保留人物的视觉身份，却常常生成变化有限、人物特异性较弱的表情。为缓解这一问题，我们提出 BEACON：一种用于特定人物视频生成的轻量级框架，通过将视觉身份与表达行为解耦，生成更具表现力的视频。BEACON 以两种互补信号作为生成条件：用于编码身份的参考图像，以及用于捕捉人物特有面部动态的参考视频。通过利用这些互补信号进行条件控制，BEACON 生成的视频能够更好地保留人物的外观及其标志性面部动态，同时还支持身份—表情迁移。我们在 MEAD 和 RAVDESS 数据集上的实验表明，通过在约 2,000 个样本对上进行微调，并仅更新预训练 Wan 视频扩散模型约 1% 的参数，BEACON 在维持具有竞争力的身份保持能力的同时，相较于最先进的视频生成方法提升了面部表情表现力。

Abstract:Generating human-centric videos that preserve both visual identity and person-specific expressive behavior remains a fundamental challenge. In addition to reproducing appearance, a model must replicate the facial behaviors that characterize how a subject expresses emotion over time. However, most state-of-the-art methods condition generation on a single reference image, which contains no information about these temporal dynamics. As a result, they tend to preserve the subject's visual identity but often produce expressions with limited variation and weak subject specificity. To mitigate this issue, we introduce BEACON, a lightweight framework for person-specific video generation that produces more expressive videos by disentangling visual identity from expressive behavior. BEACON conditions generation on two complementary signals: a reference image encoding the identity and a reference video capturing subject-specific facial dynamics. By conditioning on these complementary signals, BEACON generates videos that better preserve both the subject's appearance and characteristic facial dynamics, while also supporting identity-expression transfer. Our experiments on the MEAD and RAVDESS datasets show that by fine-tuning on approximately 2,000 pairs and updating about 1% of the pretrained Wan video diffusion model, BEACON improves facial expressivity over state-of-the-art video generation methods while maintaining competitive identity preservation.