arXiv:2110.07495
Simple Baseline for Single Human Motion Forecasting
원문 초록
Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction. Visual and social information are often used to boost model performance, however, they may consume too much computational resource. In this paper, we establish a simple but effective baseline for single human motion forecasting without visual and social information, equipped with useful training tricks. Our method "futuremotion_ICCV21" outperforms existing methods by a large margin on SoMoF benchmark. We hope our work provide new ideas for future research.
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GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction
Trajectory Prediction Meets Large Language Models: A Survey
Realistic Adversarial Attacks for Robustness Evaluation of Trajectory Prediction Models via Future State Perturbation
Multi-Strategy Improved Snake Optimizer Accelerated CNN-LSTM-Attention-Adaboost for Trajectory Prediction
ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture
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