The paper introduces Vocabulary‑Guided Gait Recognition (Gait‑World), a new paradigm that leverages human vocabularies and Vision‑Language Models (VLMs) to better understand and recognize gait. It proposes the α‑Gait model, which uses a Vocabulary Relation Mapper and a Gait Fine‑grained Detector to align vocabulary prompts with gait features, addressing the challenge of heterogeneous embedding spaces. Experiments on multiple gait datasets demonstrate the potential of incorporating VLM‑derived vocabulary information into gait recognition.
By Panjian Huang, Saihui Hou, Chunshui Cao, Xu Liu, Yongzhen Huang
arXiv:2607. 06617v1 Announce Type: cross Abstract: Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling principles remain poorly understood.
By Zongzhe Xu, Aakarsh Anand, Sarah Jiang, Chuntung Zhuang, Zitao Shuai, Sriram Sankararaman, Yuzhe Yang
The paper introduces GaitMoE, an action‑detection based mixture‑of‑experts framework for occluded gait recognition, leveraging temporal and action experts to infer missing body parts from adjacent frames and gait cycles. It also presents a new Occluded Gait database (OccGait) with diverse occlusion scenarios and annotations, and demonstrates superior performance on OccGait, OccCASIA‑B, Gait3D, and GREW datasets.
By Panjian Huang, Yunjie Peng, Saihui Hou, Chunshui Cao, Xu Liu, Zhiqiang He, Yongzhen Huang
The paper presents a new framework that classifies healthy gait and multiple musculoskeletal impairment groups using bilateral ground reaction force (GRF) and center-of-pressure (COP) signals. After normalizing and standardizing the signals, the model achieved 99.00% validation accuracy and 90.07% test accuracy with a session-level split. The approach includes class‑specific ε‑LRP explainability and synchronizes processed GRF signals with model predictions in a Blender‑based 3D visualization for sample‑level inspection.
By Nayoung Son, Minwoo Shin
MMGait is a large‑scale multi‑sensor benchmark that aligns visible, infrared, depth, LiDAR, and radar observations at the sequence level, enabling evaluation of single‑modal, cross‑modal, and multi‑modal gait recognition. The study shows that modality rankings shift with probe conditions, cross‑modal alignment remains challenging, and fusion can yield complementary gains. To address the scalability issue of training separate experts, the authors propose Omni‑Modal Gait Recognition and its implementation, OmniGait++, which unifies all recognition settings within a shared identity space using modality‑specific front ends, a shared encoder, and an anchor‑guided fusion module.
whyItMatters":"MMGait provides a common testbed for heterogeneous gait sensing and demonstrates that unified recognition across varying modality availability is feasible, offering a scalable alternative to task‑specific experts."
By Saihui Hou, Chenye Wang, Qingyuan Cai, Aoqi Li, Yongzhen Huang
arXiv:2609.10187v1 Announce Type: new
Abstract: In this work, we introduce language-aligned motion representations for domain-generalizable UPDRS-Gait severity estimation, aiming to learn semanticall...
By Soojie Kim, Muhammad Munsif, Minkyung Kim, Seungryul Baek