延迟墙：为实时虚拟化身基准测试现成的情绪识别
The Latency Wall: Benchmarking Off-the-Shelf Emotion Recognition for Real-Time Virtual Avatars
AI总结：
研究评估了现成模型在实时虚拟化身情绪识别中的延迟与准确率平衡，发现通用Transformer无法满足实时需求，需轻量级领域专用架构。
摘要：
在虚拟现实（VR）和人机交互（HCI）领域，实时情绪识别有潜力帮助自闭症谱系障碍（ASD）个体改善社交技能。这项任务需要严格的时间-准确率权衡，将运动到光子（MTP）延迟保持在140毫秒以下以维持连续性。然而，大多数现成的深度学习模型优先考虑准确性而非商用硬件的严格时间约束。作为实现可访问VR治疗的第一步，我们使用UIBVFED数据集对最先进的（SOTA）模型进行基准测试，用于在虚拟角色上进行零样本面部表情识别（FER）。我们评估了YOLO的中型和纳米变体（v8、v11和v12）用于面部检测，以及通用的视觉Transformer，包括CLIP、SigLIP和ViT-FER。我们的CPU仅推理结果显示，虽然在风格化化身上的面部检测是稳健的（100%准确率），但在分类阶段存在“延迟墙”。YOLOv11n架构在检测方面提供了最佳平衡（约54毫秒）。然而，通用Transformer如CLIP和SigLIP无法在实时循环中实现可行的准确率（<23%）或速度（>150毫秒）。本研究强调了需要轻量级、领域特定的架构，以实现可访问的实时AI在治疗环境中的应用。
英文摘要：
In the realm of Virtual Reality (VR) and Human-Computer Interaction (HCI), real-time emotion recognition shows promise for supporting individuals with Autism Spectrum Disorder (ASD) in improving social skills. This task requires a strict latency-accuracy trade-off, with motion-to-photon (MTP) latency kept below 140 ms to maintain contingency. However, most off-the-shelf Deep Learning models prioritize accuracy over the strict timing constraints of commodity hardware. As a first step toward accessible VR therapy, we benchmark State-of-the-Art (SOTA) models for Zero-Shot Facial Expression Recognition (FER) on virtual characters using the UIBVFED dataset. We evaluate Medium and Nano variants of YOLO (v8, v11, and v12) for face detection, alongside general-purpose Vision Transformers including CLIP, SigLIP, and ViT-FER.Our results on CPU-only inference demonstrate that while face detection on stylized avatars is robust (100% accuracy), a "Latency Wall" exists in the classification stage. The YOLOv11n architecture offers the optimal balance for detection (~54 ms). However, general-purpose Transformers like CLIP and SigLIP fail to achieve viable accuracy (<23%) or speed (>150 ms) for real-time loops. This study highlights the necessity for lightweight, domain-specific architectures to enable accessible, real-time AI in therapeutic settings.
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