Code for our CVPR 2019 work.
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Updated
Jul 19, 2019 - Python
Code for our CVPR 2019 work.
Reinforcement learning agent that actively learns physical properties (motion mechanisms) in a 2D simulated domain.
Code and results/visualizations for the paper "A Neural Temporal Model for Human Motion Prediction", CVPR 2019
Code for "What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction", Schöller et al., Robotics and Automation Letters (RA-L), 2020
Deep learning models for self-driving vehicles to predict other car/cyclist/pedestrian (called "agent")'s motion.
Deep learning algorithm to perform motion prediction on vehicles as part of an autonomous car software stack.
Code for: "Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs", ICCV2021 Workshops
[ICCV2021] Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
Forecasting Characteristic 3D Poses of Human Actions (CVPR'22) 🏃♂️
Music conditioned dance prediction
[ECCV 2020] Official PyTorch Implementation of "DLow: Diversifying Latent Flows for Diverse Human Motion Prediction". ECCV 2020.
Vehicle-Pedestrian Motion Prediction using Classification
Official implementation of dual quaternion transformations as described in the paper "Pose Representations for Deep Skeletal Animation".
Code for "FloMo: Tractable Motion Prediction with Normalizing Flows", Schöller et al., International Conference on Intelligent Robots and Systems (IROS), 2021
M2I is a simple but effective joint motion prediction framework through marginal and conditional predictions by exploiting the factorized relations between interacting agents.
[ECCV 2022 oral] Official PyTorch implementation of the paper "Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors"
Code for "Towards Explainable Multi-modal Motion Prediction using Graph Representations"
Implementation of "MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving" for Waymo Open Motion Dataset
Solution for Waymo Motion Prediction Challenge 2022. Our implementation of MultiPath++
Motion Prediction for Self-driving cars using Polyline-LSTM-Transformer based architecture (based on Multipath++).
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