Multi-Transmotion: 人間の動作予測のための事前学習モデル
Multi-Transmotion: Pre-trained Model for Human Motion Prediction
複数のデータセットを統合し、軌跡と3D姿勢の両方を扱うクロスモダリティ事前学習モデルを提案。様々な下流タスクで競争力のある性能を示した。
著者: Yang Gao, Po-Chien Luan, Alexandre Alahi
分類: cs.CV, cs.RO
原文アブストラクト
The ability of intelligent systems to predict human behaviors is crucial, particularly in fields such as autonomous vehicle navigation and social robotics. However, the complexity of human motion have prevented the development of a standardized dataset for human motion prediction, thereby hindering the establishment of pre-trained models. In this paper, we address these limitations by integrating multiple datasets, encompassing both trajectory and 3D pose keypoints, to propose a pre-trained model for human motion prediction. We merge seven distinct datasets across varying modalities and standardize their formats. To facilitate multimodal pre-training, we introduce Multi-Transmotion, an innovative transformer-based model designed for cross-modality pre-training. Additionally, we present a novel masking strategy to capture rich representations. Our methodology demonstrates competitive performance across various datasets on several downstream tasks, including trajectory prediction in the NBA and JTA datasets, as well as pose prediction in the AMASS and 3DPW datasets. The code is publicly available: https://github.com/vita-epfl/multi-transmotion
