motion prediction
187 papers with code • 0 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in motion prediction
Libraries
Use these libraries to find motion prediction models and implementationsDatasets
UCY
Waymo Open Motion Dataset
Argoverse 2 Motion Forecasting
Expi
SinD
GTA-IM Dataset
NTU RGB+D 2D
Lyft Level 5 Prediction
Latest papers
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
bshfang/self-supervised-motion
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Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving.
GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction. Various styles of graph convolutions have been proposed, with each one meticulously designed and incorporated into a carefully-crafted network architecture.
EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors.
Online Action Recognition for Human Risk Prediction with Anticipated Haptic Alert via Wearables
ami-iit/paper_guo_2023_humanoids_lifting_risk_prediction
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This paper proposes a framework that combines online human state estimation, action recognition and motion prediction to enable early assessment and prevention of worker biomechanical risk during lifting tasks.
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
kwwcv/ssmp
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To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module.
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
fanglaosi/skeleton-in-context
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Under this setting, the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning.
Deeper into Self-Supervised Monocular Indoor Depth Estimation
One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets.
Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
oliviazwy/dcgcn
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With potential applications in fields including intelligent surveillance and human-robot interaction, the human motion prediction task has become a hot research topic and also has achieved high success, especially using the recent Graph Convolutional Network (GCN).
Large Trajectory Models are Scalable Motion Predictors and Planners
tsinghua-mars-lab/statetransformer
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STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task.
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow
cv-stuttgart/detectiondefenses
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In this paper, we thoroughly examine the currently available detect-and-remove defenses ILP and LGS for a wide selection of state-of-the-art optical flow methods, and illuminate their side effects on the quality and robustness of the final flow predictions.
