Abstract
Generative AI (GenAI) models have revolutionized animation, enabling the synthesis of humans and motion patterns with remarkable visual fidelity. However, generating truly realistic human animation remains a formidable challenge, where even minor inconsistencies can make a subject appear unnatural. This limitation is particularly critical when AI-generated videos are evaluated for behavioral biometrics, where subtle motion cues that define identity are easily lost or distorted. The present study investigates whether state-of-the-art GenAI human animation models can preserve the subtle spatio-temporal details needed for person identification through gait biometrics. Specifically, we evaluate four different GenAI models across two primary evaluation tasks to assess their ability to i) restore gait patterns from reference videos under varying conditions of complexity, and ii) transfer these gait patterns to different visual identities. Our results show that while visual quality is mostly high, biometric fidelity remains low in tasks focusing on identification, suggesting that current GenAI models struggle to disentangle identity from motion. Furthermore, through an identity transfer task, we expose a fundamental flaw in appearance-based gait recognition: when texture is disentangled from motion, identification collapses, proving current GenAI models rely on visual attributes rather than temporal dynamics.
Abstract (translated)
生成式人工智能（GenAI）模型已经革新了动画领域，能够以惊人的视觉逼真度合成人类形象和动作模式。然而，生成真正逼真的真人动画仍然是一个艰巨的挑战，即使是细微的不一致性也会使角色显得不自然。当评估行为生物识别时，这一限制尤为关键，在这种情况下，定义身份的微妙运动线索很容易丢失或失真。本研究调查了最先进的GenAI人体动画模型能否在步态生物识别中保留用于人员识别所需的微小时空细节。具体而言，我们针对四个不同的GenAI模型进行两项主要评估任务来考察其能力：一是从参考视频中恢复步态模式，在不同复杂度条件下；二是将这些步态模式转移到不同的视觉身份上。我们的结果显示，虽然视觉质量大多很高，但在人员识别任务中生物特征的准确性仍然很低，这表明目前的GenAI模型在解耦身份和运动方面存在困难。此外，通过一项身份转换任务，我们揭示了基于外观的步态识别中的一个基本缺陷：当纹理从运动中分离出来时，识别效果会崩溃，证明当前的GenAI模型依赖于视觉属性而非时间动态。
URL
https://arxiv.org/abs/2512.19275
