SOURCE-LINKED INTELLIGENCE
When Visual Quality Misleads: Intent Recognition under Rendered Avatar Distortions
Avatar-streaming systems are commonly evaluated with image and video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a proxy for communicative success. We test this assumption through a controlled behavioral study of rendered 3D avatars across a pristine condition and fourteen geometric, photometric, temporal, and combined distortions. Fifty-nine participants contributed 2,688 judgments of perceived action, response confidence, and visual quality. We identify Misleading Quality in this dataset as distorted renderings that retain above-average perceived quality but
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-23T08:45:40.000Z
First collected: 2026-09-24T01:22:21.678Z. This is not the publication date.