OMG-Bench：面向骨架在线微手势识别的新挑战性基准
OMG-Bench: A New Challenging Benchmark for Skeleton-based Online Micro Hand Gesture Recognition
提供首个大规模骨架在线微手势基准与多视图自监督骨架生成流程，其分层记忆增强 Transformer 用帧级与窗口级记忆库保留历史上下文，可迁移到其他在线时序动作/手势检测任务。
原文依据 we develop a multi-view self-supervised pipeline to automatically generate skeleton data
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于手部骨架的在线微手势识别对 VR/AR 交互至关重要，但由于公开数据集有限且算法任务特定，仍面临挑战。微手势涉及细微运动模式，使得构建具有精确骨架和帧级标注的数据集十分困难。为此，我们开发了一种多视图自监督流程来自动生成骨架数据，并辅以启发式规则和专家精修进行半自动标注。基于该流程，我们推出了 OMG-Bench，这是首个面向骨架在线微手势识别的大规模公开基准。它包含 40 个细粒度手势类别，涵盖 1,272 个序列中的 13,948 个实例，具有细微运动、快速动态和连续执行的特点。为应对这些挑战，我们提出了分层记忆增强 Transformer（HMATr），这是一个端到端框架，通过利用存储帧级细节和窗口级语义的分层记忆库来保留历史上下文，从而统一手势检测与分类。此外，它采用从记忆初始化的可学习位置感知查询，以隐式编码手势位置和语义。实验表明，HMATr 在检测率上优于最优方法 7.6%，为在线微手势识别建立了强有力的基线。
英文原文摘要 1398 字符
Online micro gesture recognition from hand skeletons is critical for VR/AR interaction but faces challenges due to limited public datasets and task-specific algorithms. Micro gestures involve subtle motion patterns, which make constructing datasets with precise skeletons and frame-level annotations difficult. To this end, we develop a multi-view self-supervised pipeline to automatically generate skeleton data, complemented by heuristic rules and expert refinement for semi-automatic annotation. Based on this pipeline, we introduce OMG-Bench, the first large-scale public benchmark for skeleton-based online micro gesture recognition. It features 40 fine-grained gesture classes with 13,948 instances across 1,272 sequences, characterized by subtle motions, rapid dynamics, and continuous execution. To tackle these challenges, we propose Hierarchical Memory-Augmented Transformer (HMATr), an end-to-end framework that unifies gesture detection and classification by leveraging hierarchical memory banks which store frame-level details and window-level semantics to preserve historical context. In addition, it employs learnable position-aware queries initialized from the memory to implicitly encode gesture positions and semantics. Experiments show that HMATr outperforms state-of-the-art methods by 7.6% in detection rate, establishing a strong baseline for online micro gesture recognition.