TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild

Author:

Adeli Vida1,Ehsanpour Mahsa2,Reid Ian2,Niebles Juan Carlos3,Savarese Silvio3,Adeli Ehsan3,Rezatofighi Hamid4

Affiliation:

1. Ferdowsi University of Mashhad

2. University of Adelaide

3. Stanford University

4. Monash University

Publisher

IEEE

Reference67 articles.

1. It is not the journey but the destination: Endpoint conditioned trajectory prediction;mangalam;ECCV,2020

2. Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision

3. Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks;kosaraju;NeurIPS,2019

4. Deep neural networks enable quantitative movement analysis using single-camera videos;kidzi?ski;Nature Communications,2020

5. Structural-rnn: Deep learning on spatio-temporal graphs;jain;CVPR,2016

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1. Analysis of Multi-Person Pose Forecasting Models on Handball Actions;2024 8th International Conference on Computer, Software and Modeling (ICCSM);2024-07-04

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3. Robots That Can See: Leveraging Human Pose for Trajectory Prediction;IEEE Robotics and Automation Letters;2023-11

4. Exploiting Spatio-Temporal Human-Object Relations Using Graph Neural Networks for Human Action Recognition and 3D Motion Forecasting;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

5. Reconstructing Groups of People with Hypergraph Relational Reasoning;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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