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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2601.15914 |
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| _version_ | 1866911392236306432 |
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| author | Benyamin, Yarin |
| author_facet | Benyamin, Yarin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Latency Wall: Benchmarking Off-the-Shelf Emotion Recognition for Real-Time Virtual Avatars Benyamin, Yarin Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | The Latency Wall: Benchmarking Off-the-Shelf Emotion Recognition for Real-Time Virtual Avatars |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.15914 |