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Benchmarks
Greatest papers with code
Tracking without bells and whistles
ICCV 2019
• phil-bergmann/tracking_wo_bnw
• 
We then perform an analysis on the performance and failure cases of several state-of-the-art tracking methods in comparison to our Tracktor.
Ranked #1 on
Online Multi-Object Tracking
on MOT17
On human motion prediction using recurrent neural networks
CVPR 2017
• facebookresearch/QuaterNet
•
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.
HUMAN MOTION PREDICTION MOTION ESTIMATION MOTION PREDICTION MOTION SYNTHESIS
One Thousand and One Hours: Self-driving Motion Prediction Dataset
• lyft/l5kit
•
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.
AUTONOMOUS VEHICLES MOTION FORECASTING MOTION PLANNING MOTION PREDICTION
Animating Arbitrary Objects via Deep Motion Transfer
CVPR 2019
• AliaksandrSiarohin/monkey-net
•
This is achieved through a deep architecture that decouples appearance and motion information.
GRIP: Graph-based Interaction-aware Trajectory Prediction
IEEE Intelligent Transportation Systems Conference (ITSC) 2019
• rohanchandra30/Spectral-Trajectory-and-Behavior-Prediction
•
The prediction error of GRIP is one meter shorter than existing schemes.
3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams
In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT).
Long-term Human Motion Prediction with Scene Context
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene.
Learning Trajectory Dependencies for Human Motion Prediction
ICCV 2019
• wei-mao-2019/LearnTrajDep
•
In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints.
Ranked #1 on
Human Pose Forecasting
on Human3.6M
HUMAN MOTION PREDICTION HUMAN POSE FORECASTING MOTION CAPTURE MOTION PREDICTION
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
CVPR 2020
• pxiangwu/MotionNet
•
The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion.
Collaborative Motion Prediction via Neural Motion Message Passing
Motion prediction is essential and challenging for autonomous vehicles and social robots.