핵심 개념
LAVIMO introduces a novel framework for three-modality learning, integrating human-centric videos to enhance alignment between text and motion modalities.
초록
Information retrieval is crucial, leading to a surge in human motion research.
LAVIMO integrates text, video, and motion modalities for enhanced alignment.
A specially designed attention mechanism is used for feature fusion.
Results show state-of-the-art performance in various cross-modal retrieval tasks.
Experiments conducted on HumanML3D and KIT-ML datasets.
Comparison with prior works like TMR, MotionCLIP, and MotionSet.
Qualitative results demonstrate the effectiveness of LAVIMO.
User study confirms the framework's generalization to real-life videos.
Limitations include the use of rendered videos instead of real-life footage.
통계
LAVIMO achieves state-of-the-art performance in various motion-related cross-modal retrieval tasks.
HumanML3D dataset features 14,616 motions and 44,970 textual descriptions.
KIT-ML dataset contains 3,911 sequences and 6,278 text annotations.
인용구
"Our key contributions are summarized as follows: We introduce LAnguage-VIdeo-MOtion Alignment (LAVIMO), a framework designed to cultivate a cohesive embedding space across the three aforementioned modalities."
"Our model demonstrates precision in retrieving the exact ground-truth motion in the rank1 position."