HEMVIP: Human Evaluation of Multiple Videos in Parallel

Abstract

In many research areas, for example motion and gesture generation, objective measures alone do not provide an accurate impression of key stimulus traits such as perceived quality or appropriateness. The gold standard is instead to evaluate these aspects through user studies, especially subjective evaluations of video stimuli. Common evaluation paradigms either present individual stimuli to be scored on Likert-type scales, or ask users to compare and rate videos in a pairwise fashion. However, the time and resources required for such evaluations scale poorly as the number of conditions to be compared increases. Building on standards used for evaluating the quality of multimedia codecs, this paper instead introduces a framework for granular rating of multiple comparable videos in parallel. This methodology essentially analyses all condition pairs at once. Our contributions are 1) a proposed framework, called HEMVIP, for parallel and granular evaluation of multiple video stimuli and 2) a validation study confirming that results obtained using the tool are in close agreement with results of prior studies using conventional multiple pairwise comparisons.

arXiv comment6 pages, 1 figures. Proceedings of the 22th ACM International Conference on Multimodal Interaction. 2021. Montreal, Canada

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Cited by in corpus (8)

• A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
• The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation
• Evaluating gesture generation in a large-scale open challenge: The GENEA Challenge 2022
• Diffusion-Based Co-Speech Gesture Generation Using Joint Text and Audio Representation
• Evaluating Data-Driven Co-Speech Gestures of Embodied Conversational Agents through Real-Time Interaction
• The ReprGesture entry to the GENEA Challenge 2022
• Integrated Speech and Gesture Synthesis
• Gesture Evaluation in Virtual Reality