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

• URL: http://arxiv.org/abs/2407.08101v3
• Date: Mon, 23 Dec 2024 17:06:20 GMT
• Title: What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction
• Authors: 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,
• Abstract summary: We present the QEVD benchmark and dataset, which explores human-AI interaction in the challenging, yet controlled, real-world domain of fitness coaching.<n>The benchmark requires vision-language models to recognize complex human actions, identify possible mistakes, and provide appropriate feedback in real-time.<n>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.
• Score: 5.958765450103163
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Proact-VL: A Proactive VideoLLM for Real-Time AI Companions [52.23988809605433]
  We instantiate AI companions through two gaming scenarios, commentator and guide, selected for automatic evaluation.<n>We present Proact-VL, a framework that shapes multimodal language models into proactive, real-time interactive agents capable of human-like environment perception and interaction.
  arXiv  Detail & Related papers  (2026-03-03T19:02:46Z)
• Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models [80.28579390566298]
  We introduce Interact2Ar, a text-conditioned autoregressive diffusion model for generating full-body, human-human interactions.<n>Hand kinematics are incorporated through dedicated parallel branches, enabling high-fidelity full-body generation.<n>Our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios.
  arXiv  Detail & Related papers  (2025-12-22T18:59:50Z)
• VITA-E: Natural Embodied Interaction with Concurrent Seeing, Hearing, Speaking, and Acting [66.90028121194636]
  Current Vision-Language-Action (VLA) models are often constrained by a rigid, static interaction paradigm.<n>VITA-E is a novel embodied interaction framework designed for both behavioral and nearly real-time interruption.
  arXiv  Detail & Related papers  (2025-10-21T17:59:56Z)
• FLEXI: Benchmarking Full-duplex Human-LLM Speech Interaction [49.83226596963294]
  Speech-computer human interaction enables real-time spoken dialogue systems.<n>Modelling and benchmarking these models remains a fundamental challenge.<n>We introduce FLEXI, the first benchmark for full-human spoken interaction.
  arXiv  Detail & Related papers  (2025-09-26T11:57:42Z)
• Adapting Vision-Language Models for Evaluating World Models [24.813041196394582]
  We present UNIVERSE, a method for adapting Vision-language Evaluator for Rollouts in Simulated Environments under data and compute constraints.<n>We conduct a large-scale study comparing full, partial, and parameter-efficient finetuning across task formats, context lengths, sampling strategies, and data compositions.<n>The resulting unified evaluator matches the performance of task-specific baselines using a single checkpoint.
  arXiv  Detail & Related papers  (2025-06-22T09:53:28Z)
• Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilities [93.09944267871163]
  FullDuplexBench is a benchmark that systematically evaluates key conversational behaviors. We aim to advance spoken dialogue modeling and encourage the development of more interactive and natural dialogue systems.
  arXiv  Detail & Related papers  (2025-03-06T18:59:16Z)
• Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues [54.81155589931697]
  Collaborative Instance object Navigation (CoIN) is a new task setting where the agent actively resolve uncertainties about the target instance. We propose a novel training-free method, Agent-user Interaction with UncerTainty Awareness (AIUTA) First, upon object detection, a Self-Questioner model initiates a self-dialogue within the agent to obtain a complete and accurate observation description. An Interaction Trigger module determines whether to ask a question to the human, continue or halt navigation.
  arXiv  Detail & Related papers  (2024-12-02T08:16:38Z)
• Visual Contexts Clarify Ambiguous Expressions: A Benchmark Dataset [0.39462888523270856]
  We propose VAGUE, a multimodal benchmark comprising 3.9K indirect human utterances paired with corresponding scenes. Our work aims to delve deeper into the ability of models to understand indirect communication and seek to contribute to the development of models capable of more refined and human-like interactions.
  arXiv  Detail & Related papers  (2024-11-21T14:01:42Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues. We propose a novel approach to address this issue at test time without requiring retraining. MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Controllable Human-Object Interaction Synthesis [77.56877961681462]
  We propose Controllable Human-Object Interaction Synthesis (CHOIS) to generate synchronized object motion and human motion in 3D scenes. Here, language descriptions inform style and intent, and waypoints, which can be effectively extracted from high-level planning, ground the motion in the scene. Our module seamlessly integrates with a path planning module, enabling the generation of long-term interactions in 3D environments.
  arXiv  Detail & Related papers  (2023-12-06T21:14:20Z)
• Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction [2.1261712640167856]
  Emotion recognition is a crucial task for human conversation understanding. We propose an input Temporal Graph Neural Network with Cross-Modality Interaction (CORECT) CORECT effectively captures conversation-level cross-modality interactions and utterance-level temporal dependencies.
  arXiv  Detail & Related papers  (2023-11-08T07:46:25Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors [36.75629570208193]
  Human-object interaction (HOI) detection aims to extract interacting human-object pairs and their interaction categories from a given natural image. In this paper, we tackle HOI detection with the weakest supervision setting in the literature, using only image-level interaction labels. We first propose an approach to prune non-interacting human and object proposals to increase the quality of positive pairs within the bag, exploiting the grounding capability of the vision-language model. Second, we use a large language model to query which interactions are possible between a human and a given object category, in order to force the model not to put emphasis
  arXiv  Detail & Related papers  (2023-03-09T19:08:02Z)
• A Probabilistic Model Of Interaction Dynamics for Dyadic Face-to-Face Settings [1.9544213396776275]
  We develop a probabilistic model to capture the interaction dynamics between pairs of participants in a face-to-face setting. This interaction encoding is then used to influence the generation when predicting one agent's future dynamics. We show that our model successfully delineates between the modes, based on their interacting dynamics.
  arXiv  Detail & Related papers  (2022-07-10T23:31:27Z)
• Teaching Robots to Grasp Like Humans: An Interactive Approach [3.3836709236378746]
  This work investigates how the intricate task of grasping may be learned from humans based on demonstrations and corrections. Rather than training a person to provide better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration.
  arXiv  Detail & Related papers  (2021-10-09T10:27:50Z)
• Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions [81.88294320397826]
  A system does not know what human-object interactions are present in a video as or the actual location of the human and object. We introduce a dataset comprising over 6.5k videos with human-object interaction that have been curated from sentence captions. We demonstrate improved performance over weakly supervised baselines adapted to our annotations on our video dataset.
  arXiv  Detail & Related papers  (2021-10-07T15:30:18Z)
• Learning Asynchronous and Sparse Human-Object Interaction in Videos [56.73059840294019]
  Asynchronous-Sparse Interaction Graph Networks (ASSIGN) is able to automatically detect the structure of interaction events associated with entities in a video scene. ASSIGN is tested on human-object interaction recognition and shows superior performance in segmenting and labeling of human sub-activities and object affordances from raw videos.
  arXiv  Detail & Related papers  (2021-03-03T23:43:55Z)
• Learning Temporal Dynamics from Cycles in Narrated Video [85.89096034281694]
  We propose a self-supervised solution to the problem of learning to model how the world changes as time elapses. Our model learns modality-agnostic functions to predict forward and backward in time, which must undo each other when composed. We apply the learned dynamics model without further training to various tasks, such as predicting future action and temporally ordering sets of images.
  arXiv  Detail & Related papers  (2021-01-07T02:41:32Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.