arXiv:2609.18432v1 Announce Type: new
Abstract: Extensive occlusions in real-world scenarios pose challenges to gait recognition due to missing and noisy information, as well as body misalignment in...
By Panjian Huang, Yunjie Peng, Saihui Hou, Chunshui Cao, Xu Liu, Zhiqiang He, 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
arXiv:2609.18413v1 Announce Type: new
Abstract: What is a gait? Appearance-based gait networks consider a gait as the human shape and motion information from images. Model-based gait networks treat a...
By Panjian Huang, Saihui Hou, Chunshui Cao, Xu Liu, Yongzhen Huang
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:2609.18490v1 Announce Type: new
Abstract: "What I cannot create, I do not understand."Human wisdom reveals that creation is one of the highest forms of learning. For example, Diffusion Models h...
By Panjian Huang, Saihui Hou, Junzhou Huang, Yongzhen Huang
The paper introduces the Identity-Aware Human-Object Interaction Motion Captioning task, which requires captions to include both the subject’s identity and the interaction motion, e.g., "Sub_ID lifts the chair" instead of a generic description. It proposes ID‑HOINet, a model that learns from multi‑view videos using a Multi‑View Identity‑Motion Learning Module and a Two‑Stage Caption Rewriting Strategy to generate identity‑aware captions. Experiments show that ID‑HOINet achieves state‑of‑the‑art performance on the BEHAVE and InterCap datasets.
By Yiming Wang, Yonghao Dang, Huilai Li, Jiawei Tu, Jianqin Yin