arXiv Computer Vision
Ampe-DB: A Dataset for Human Activity and Interaction Analysis Using the Ghanaian Game Ampe
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SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities
arXiv:2609.08038v2 Announce Type: cross Abstract: Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as fa...
arXiv Computer Vision
Sep 17
Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective
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.
arXiv AI
Jul 21
STAR: Skeletal Token Alignment and Rearrangement for Interaction Recognition
arXiv:2607. 17342v1 Announce Type: cross Abstract: Understanding physical human-robot and human-human interactions is a challenging yet emerging topic in 3D vision.
arXiv AI
Oct 28
SOMA: A Benchmark for Fine-Grained Egocentric Behavior and Body-Motion Understanding
SOMA is a new benchmark that tests fine‑grained egocentric body‑motion understanding, featuring 1,632 human‑verified questions across body configuration, motion dynamics, and temporal reasoning. The study evaluates 37 multimodal large language models (MLLMs) and finds that humans achieve 68.5% accuracy while the best MLLM (Qwen3.6‑27B) reaches only 50.0% zero‑shot accuracy. The authors also provide a training dataset and a multimodal baseline to help close the performance gap and highlight common failure modes such as misreading body parts and temporal errors.
arXiv AI
Jul 1
SkillSpotter: Pose-Aware Multi-View Skilled Action Detection and Grading in Ego-Exo Videos
arXiv:2606. 31127v1 Announce Type: cross Abstract: To enable personalized, real-time coaching using Augmented Reality glasses or fixed camera setups in domains such as sports, cooking, or music, a system must understand not just what a person does, but how well they execute an activity.
arXiv Computer Vision
2d ago
Movens-Zero: Scaling Humanoid Motion Foundation Models with Large-Scale Human Videos
The paper introduces Movens-5M, a dataset of 15 million human‑motion sequences and 5 million humanoid‑executed motions derived from raw Internet videos, aligned with text, RGB video, speech, and music. Using this data, the authors present Movens‑Zero, a scalable architecture that jointly learns across modalities and supports real‑time closed‑loop execution, enabling motion generation from text, speech, music, and video. They also release Movens‑Intent, a benchmark of 8,192 expert‑verified condition‑motion pairs, and demonstrate that scaling data quality and model capacity improves performance across all modalities.