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
Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting
Human motion prediction is still an open problem extremely important for autonomous driving and safety applications.
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
hkust-aerial-robotics/simpl
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This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles.
Using Motion Forecasting for Behavior-Based Virtual Reality (VR) Authentication
Terascale-All-sensing-Research-Studio/Forecasting_for_Authentication
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In this work, we present the first approach that predicts future user behavior using Transformer-based forecasting and using the forecasted trajectory to perform user authentication.
Visual Point Cloud Forecasting enables Scalable Autonomous Driving
To resolve this, we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input.
Expressive Forecasting of 3D Whole-body Human Motions
dingpx/eai
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Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications.
Energy-based Potential Games for Joint Motion Forecasting and Control
This work uses game theory as a mathematical framework to address interaction modeling in multi-agent motion forecasting and control.
MoMask: Generative Masked Modeling of 3D Human Motions
For the base-layer motion tokens, a Masked Transformer is designated to predict randomly masked motion tokens conditioned on text input at training stage.
Learning Cooperative Trajectory Representations for Motion Forecasting
Specifically, we present V2X-Graph, the first interpretable and end-to-end learning framework for cooperative motion forecasting.
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
ziqipang/lm4visualencoding
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This paper reveals that large language models (LLMs), despite being trained solely on textual data, are surprisingly strong encoders for purely visual tasks in the absence of language.
Streaming Motion Forecasting for Autonomous Driving
ziqipang/streamingforecasting
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Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks.
