arXiv Computer Vision
Vocabulary-Guided Gait Recognition
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
1d ago
Learning A Unified Template for Gait Recognition
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...
arXiv Computer Vision
1d ago
Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective
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...
arXiv Computer Vision
Aug 21
Silhouette-based Gait Foundation Model
arXiv:2512. 00691v2 Announce Type: replace Abstract: Gait patterns play a critical role in human identification and healthcare analytics, yet current progress remains constrained by small, narrowly designed models that fail to scale or generalize.
arXiv Computer Vision
Aug 28
Who Remains, What Changes: Identity Anchored Composed Gait Retrieval
The paper introduces Composed Gait Retrieval (CoGR), a task that retrieves a target gait sequence using a reference sequence and a natural language modification query. To support this, the authors create the first gait-language datasets—Language‑Augmented CCPG and Language‑Augmented CASIA‑B—via an automated annotation pipeline powered by large vision‑language models. They propose ComposeGait, an identity‑anchored composition framework with a Part‑aware Identity Adapter that injects identity tokens into a shared Q‑Former, achieving state‑of‑the‑art retrieval performance on both benchmarks.
arXiv Computer Vision
Sep 10
3rd Place Solution to Human Motion Challenges in Real-World and Clinical Settings (MoCha) @ECCV2026: Language-Aligned Motion Representations for Domain-Generalizable UPDRS-Gait Severity Estimation
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...
arXiv Computer Vision
Sep 11
MMGait: Benchmarking and Unifying Gait Recognition across Heterogeneous Modalities
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."