Understanding human motion processing is essential for building reliable, human-centered computer vision systems. Although deep neural networks (DNNs) achieve strong performance in optical flow estimation, they remain less robust than humans and rely on fundamentally different computational strategies. Visual motion illusions provide a powerful probe into these mechanisms, revealing how human and machine vision align or diverge. While recent DNN-based motion models can reproduce dynamic illusions such as reverse-phi, it remains unclear whether they can perceive illusory motion in static images, exemplified by the Rotating Snakes illusion. We evaluate several representative optical flow models on Rotating Snakes and show that most fail to generate flow fields consistent with human perception. Under simulated conditions mimicking saccadic eye movements, only the human-inspired Dual-Channel model exhibits the expected rotational motion, with the closest correspondence emerging during the saccade simulation. Ablation analyses further reveal that both luminance-based and higher-order color--feature--based motion signals contribute to this behavior and that a recurrent attention mechanism is critical for integrating local cues. Our results highlight a substantial gap between current optical-flow models and human visual motion processing, and offer insights for developing future motion-estimation systems with improved correspondence to human perception and human-centric AI.

翻译：理解人类的运动处理机制对于构建可靠、以人为中心的计算机视觉系统至关重要。尽管深度神经网络（DNN）在光流估计任务中表现出色，但其鲁棒性仍不如人类，且依赖根本不同的计算策略。视觉运动错觉为探究这些机制提供了有力工具，揭示了人类与机器视觉的异同。尽管近期基于DNN的运动模型能够再现诸如反向phi之类的动态错觉，但它们能否感知静态图像中的错觉运动（如“旋转蛇”错觉）尚不明确。我们评估了多个代表性光流模型在“旋转蛇”图形上的表现，结果表明大多数模型无法生成与人类感知一致的光流场。在模拟扫视眼动的条件下，仅有人类启发的双通道模型展现出预期的旋转运动，且在扫视模拟过程中与人类感知的对应最为接近。消融分析进一步揭示，基于亮度和基于高阶颜色特征的信号均对此行为有贡献，而循环注意力机制对于整合局部线索至关重要。我们的研究结果凸显了当前光流模型与人类视觉运动处理之间的显著差距，并为开发与人类感知更一致的运动估计系统及以人为本的人工智能提供了启示。