Abstract
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 this http URL 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.
Abstract (translated)
在虚拟现实（VR）和人机交互（HCI）领域，实时情绪识别对帮助自闭症谱系障碍（ASD）患者提高社交技能具有潜力。这一任务需要严格处理延迟与精度之间的权衡问题，即运动到光子(MTP)的延迟应保持在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更注重准确性而非消费品硬件严格的定时约束条件。作为迈向可访问VR疗法的第一步，我们使用UIBVFED数据集对虚拟角色进行零样本面部表情识别（FER）任务，来基准测试最新的状态-of-the-art (SOTA) 模型。我们评估了YOLO的Medium和Nano变体（v8, v11 和 v12），用于脸部检测，并包括通用视觉转换器如CLIP、SigLIP以及另一个未明确指明的模型。 仅使用CPU进行推断的结果显示，尽管在风格化的头像上实现面部检测是稳健的（准确率为100%），但在分类阶段存在一个“延迟墙”。YOLOv11n架构提供了最佳平衡以用于检测（约为54毫秒）。然而，通用视觉转换器如CLIP和SigLIP未能达到实时循环中的可接受精度（小于23%）或速度（大于150毫秒）。这项研究强调了为了实现可访问的、实时的人工智能在治疗环境中的应用，需要开发轻量级且特定领域的架构。
URL
https://arxiv.org/abs/2601.15914
