T-MOR: 動作を考慮した骨格表現の学習による人間行動認識
T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition
骨格系列から転移可能な行動表現を学ぶフレームワークT-MORを提案。ビデオと言語の教師信号で骨格表現を整列させ、軽量な骨格入力のみで高精度な行動認識を実現する。
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著者: Di Yang, Mahmoud Ali, Quan Kong, Gianpiero Francesca, Francois Bremond
分類: cs.CV
原文アブストラクト
Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing models rely primarily on appearance-level supervision from images or videos, and do not explicitly model human motion, which is essential for fine-grained and human-centric action recognition task as actions are defined by temporally structured and physically grounded body movements. To address this problem, we propose Transferable skeleton MOtion Representation (T-MOR), a motion-aware framework that learns transferable action representations from skeleton sequences with the aid of video and language supervision during training. T-MOR adopts a multi-modal contrastive learning scheme that aligns skeleton motion with visual and textual representations, while performing inference using only lightweight skeleton inputs. To support large-scale pre-training, we construct PoseCap-1M, a new dataset that contains over one million synchronized video, skeleton, and text triplets covering diverse human activities. We evaluate T-MOR on a range of human-centric action recognition benchmarks, including action classification and frame-wise temporal detection. Experimental results show that T-MOR consistently improves performance across multiple datasets, such as Toyota Smarthome, Penn Action, UAV-Human, TSU, and Charades. In addition, T-MOR demonstrates strong generalization ability in few-shot and zero-shot settings, highlighting the effectiveness of motion-centric and embodied representations for transferable action understanding.
