AI RESEARCH
Pioneering Perceptual Video Fluency Assessment: A Novel Task with Benchmark Dataset and Baseline
arXiv CS.CV
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ArXi:2603.26055v1 Announce Type: new Accurately estimating humans' subjective feedback on video fluency, e.g., motion consistency and frame continuity, is crucial for various applications like streaming and gaming. Yet, it has long been overlooked, as prior arts have focused on solving it in the video quality assessment (VQA) task, merely as a sub-dimension of overall quality. In this work, we conduct pilot experiments and reveal that current VQA predictions largely underrepresent fluency, thereby limiting their applicability.