MyoMechanix is a multimodal dataset and framework for action quality assessment that incorporates muscle activity and other physiological signals alongside visual data. It contains over 7,500 samples of 20 weight‑loaded actions from 38 subjects, with synchronized RGB video, 3D pose, sEMG, and additional signals. The accompanying Fitness Knowledge Graph structures expert annotations into relationships among actions, phases, key steps, errors, and corrective feedback, enabling compositional scoring and interpretable assessment through the CUBIST engine. The project also introduces MyoMechanix‑AQA, MyoMechanix‑VideoQA, and a novel MyoMechanix‑Video2EMG task, demonstrating that multimodal sensing and structured representations improve performance, interpretability, and error attribution.
By Hao Yin, Paritosh Parmar, Lijun Gu, Lin Xu, Tianxiao Guo, Xiujin Liu, Tianyou Zheng, Yang Zhang, Weiwei Fu
DrGait is a training‑free framework that transforms Vision‑Language Models into clinical planners for gait analysis. It separates semantic reasoning from geometric perception using a Triage‑Verification‑Synthesis workflow, where hypotheses are generated, verified with deterministic biomechanical tools, and refined in a closed‑loop. This approach reduces hallucinations and produces transparent, audit‑ready clinical reports with competitive diagnostic accuracy.
By Xiangyu Yin, Shiqi Wang, Abrar Alamri, Yasir Aljohani, Weichen Liu, Goeran Fiedler, Wei Gao
arXiv:2607. 16322v1 Announce Type: cross Abstract: Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise.
By Taorui Wang, Wei Xia, Hui Ma, Zijia Song, Jiayu Zhang, Zeheng Wang, Yong Xu, Zitong Yu
arXiv:2609.39378v1 Announce Type: new
Abstract: Real-world embodied tasks, from everyday activities to professional procedures, require agents to act under physical constraints while tracking evolvin...
By Shulin Tian, Junsu Kim, Shuai Liu, Hao Li, Yujiao Shen, Sihan Li, Zhe Yang, Yeongon Kim, Feiyu Li, Jialin Wu, Yichi Zhang, Wenhui Wang, Runmao Yao, Yuhao Dong, Zhaoxi Chen, Fangzhou Hong, Antonino Furnari, Jingkang Yang, Hongyuan Zhu, Ziwei Liu
PhysVista is a new benchmark that evaluates physical intelligence in Vision‑Language Models (VLMs) by integrating perception, reasoning, and plausibility assessment into a closed cognitive loop. It distinguishes between event‑level and scale‑level reasoning and tests models on both real‑world and AI‑generated videos to provide a holistic, fine‑grained analysis of physical understanding. Experiments show significant gaps in VLMs’ physical reasoning and plausibility assessment, underscoring the need for more principled, physically grounded multimodal designs.
By Xinge Peng, Yiting Lu, Tianwu Zhi, Wen Wen, Jianzhao Liu, Xin Li, Zhibo Chen
PhysioAI introduces a clinical knowledge‑guided framework that injects structured physiotherapy knowledge into skeleton‑based action recognition models. By combining graph‑based spatiotemporal modeling with semantic anchors derived from a Clinical Knowledge Dictionary encoded via a frozen CLIP model, PhysioAI improves training of skeleton representations while requiring only skeleton inputs at inference. In subject‑disjoint evaluations, it outperforms existing methods on KiMoRe, Hard‑67, and UI‑PRMD benchmarks, achieving up to 99.03% accuracy on KiMoRe overall.
By Jie Cao, Euijoon Ahn, Anwar Hassan, Jinman Kim
arXiv:2606. 28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent.
By Deep Ghosal, Ishani Sen, Wazib Ansar, Amlan Chakrabarti
The paper introduces ProAction, a multimodal dataset of 10,000 samples comprising visual, audio, and text inputs across 12 daily-life scenarios, designed to support the Proactive Robot Action Reasoning (ProRobo) problem. It presents a two-stage human-in-the-loop annotation pipeline that incorporates appraisal and Theory-of-Mind considerations to generate cognitively grounded high-level action labels. The authors benchmark multimodal large language models and propose MMC2Act, showing that training on ProAction significantly improves proactive action reasoning compared to general-purpose models.
By Zhihao Gu, Kechao Zhu, Yuanfeng Wu, Mohan Liu, Ankit Kumar Shaw, ChenDong Hong, Xuanyu Chen, Dengchen Mei, Xu Tianyi, Lin Wang
arXiv:2606. 07433v1 Announce Type: cross Abstract: Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios.
By Jiahao Meng, Yue Tan, Qi Xu, Kuan Gao, Weisong Liu, Yanwei Li, Jason Li, Lingdong Kong, Haochen Wang, Qianyu Zhou, Jiangning Zhang, Guangliang Cheng, Yunhai Tong, Lu Qi, Minghsuan Yang
Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of physical regularities. However, despite their strong performance on general video understanding tasks, current video-language models still struggle to reliably determine whether an observed event conforms to specific physical laws.
arXiv:2604. 05360v2 Announce Type: replace-cross Abstract: Gait analysis is essential in post-stroke rehabilitation but remains time-intensive and cognitively demanding, especially when clinicians must integrate gait videos and motion-capture data into structured reports.
By Khoi T. N. Nguyen, Nghia D. Nguyen, Hui Yu Koh, Patrick W. H. Kwong, Karen Sui Geok Chua, Ananda Sidarta, Baosheng Yu
arXiv:2509. 12263v3 Announce Type: replace Abstract: Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge.
By Gautam Sreekumar, Vishnu Naresh Boddeti