Motion Forecasting
66 papers with code • 1 benchmarks • 12 datasets
Motion forecasting is the task of predicting the location of a tracked object in the future
Benchmarks
These leaderboards are used to track progress in Motion Forecasting
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Datasets
Argoverse
Waymo Open Motion Dataset
Argoverse-HD
Argoverse 2 Motion Forecasting
SinD
Argoverse 2 Sensor
Lyft Level 5 Prediction
Argoverse 2 Lidar
Latest papers
Staged Contact-Aware Global Human Motion Forecasting
l-scofano/stag
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So far, only Mao et al. NeurIPS'22 have addressed scene-aware global motion, cascading the prediction of future scene contact points and the global motion estimation.
Efficient Baselines for Motion Prediction in Autonomous Driving
However, despite many approaches use simple ConvNets and LSTMs to obtain the social latent features, State-Of-The-Art (SOTA) models might be too complex for real-time applications when using both sources of information (map and past trajectories) as well as little interpretable, specially considering the physical information.
Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders
jchengai/forecast-mae
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ICCV 2023
This study explores the application of self-supervised learning (SSL) to the task of motion forecasting, an area that has not yet been extensively investigated despite the widespread success of SSL in computer vision and natural language processing.
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
nvlabs/trajdata
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NeurIPS 2023
The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking.
QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction
ZikangZhou/QCNet
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For the first time, we show that a joint prediction model can outperform marginal prediction models even on the marginal metrics, which opens up new research opportunities in trajectory prediction.
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?
In fact, conventional forecasting methods are usually not trained nor tested in real-world pipelines (e. g., with upstream detection, tracking, and mapping modules).
Stochastic Multi-Person 3D Motion Forecasting
Sirui-Xu/DuMMF
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This paper aims to deal with the ignored real-world complexities in prior work on human motion forecasting, emphasizing the social properties of multi-person motion, the diversity of motion and social interactions, and the complexity of articulated motion.
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
quan-dao/practical-collab-perception
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CVPR 2023
The MoDAR modality propagates object information from temporal contexts to a target frame, represented as a set of virtual points, one for each object from a waypoint on a forecasted trajectory.
Best Practices for 2-Body Pose Forecasting
edodema/BestPractices2Body
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The task of collaborative human pose forecasting stands for predicting the future poses of multiple interacting people, given those in previous frames.
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
sshirahmad/GCRL
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First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment.
