What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction

Sunny Panchal; Apratim Bhattacharyya; Guillaume Berger; Antoine Mercier; Cornelius Bohm; Florian Dietrichkeit; Reza Pourreza; Xuanlin Li; Pulkit Madan; Mingu Lee; Mark Todorovich; Ingo Bax; Roland Memisevic

arXiv · 2407.08101

What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction

Sunny Panchal·Apratim Bhattacharyya·Guillaume Berger·Antoine Mercier·Cornelius Bohm·Florian Dietrichkeit·Reza Pourreza·Xuanlin Li·Pulkit Madan·Mingu Lee·Mark Todorovich·Ingo Bax·Roland Memisevic

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Abstract

Vision-language models have shown impressive progress in recent years. However, existing models are largely limited to turn-based interactions, where each turn must be stepped (i.e., prompted) by the user. Open-ended, asynchronous interactions, where an AI model may proactively deliver timely responses or feedback based on the unfolding situation in real-time, are an open challenge. In this work, we present the QEVD benchmark and dataset, which explores human-AI interaction in the challenging, yet controlled, real-world domain of fitness coaching -- a task which intrinsically requires monitoring live user activity and providing immediate feedback. The benchmark requires vision-language models to recognize complex human actions, identify possible mistakes, and provide appropriate feedback in real-time. Our experiments reveal the limitations of existing state-of-the-art vision-language models for such asynchronous situated interactions. Motivated by this, we propose a simple end-to-end streaming baseline that can respond asynchronously to human actions with appropriate feedback at the appropriate time.

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Sunny Panchal, Apratim Bhattacharyya, Guillaume Berger, Antoine Mercier, Cornelius Bohm, Florian Dietrichkeit, Reza Pourreza, Xuanlin Li, Pulkit Madan, Mingu Lee, Mark Todorovich, Ingo Bax, Roland Memisevic. 2026-04-14. What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction. https://arxiv.org/abs/2407.08101

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