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Motion Forecasting

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하위 태스크: 2
평가표: 1
보존된 평가 기록: 296

Motion forecasting is the task of predicting the location of a tracked object in the future

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/EricGuo5513/momask-codes

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MoMask: Generative Masked Modeling of 3D Human Motions

https://github.com/GUO-W/MultiMotion

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Multi-Person Extreme Motion Prediction

https://github.com/JudasDie/SOTS

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One More Check: Making "Fake Background" Be Tracked Again

https://github.com/LucaHermes/lightweight-motion-forecasting

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Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting

https://github.com/Sirui-Xu/DuMMF

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Stochastic Multi-Person 3D Motion Forecasting

https://github.com/StanfordASL/Trajectron

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The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs

https://github.com/StanfordASL/Trajectron-plus-plus

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Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

https://github.com/TUM-AAS/motron-cvpr22

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Motron: Multimodal Probabilistic Human Motion Forecasting

https://github.com/Terascale-All-sensing-Research-Studio/Forecasting_for_Authentication

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Using Motion Forecasting for Behavior-Based Virtual Reality (VR) Authentication

https://github.com/ZikangZhou/QCNet

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QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction

https://github.com/abduallahmohamed/Social-STGCNN

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Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

https://github.com/agrimgupta92/sgan

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Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

https://github.com/air-discover/llm-augmented-mtr

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Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving

https://github.com/air-thu/v2x-graph

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Learning Cooperative Trajectory Representations for Motion Forecasting

https://github.com/alafage/hlt-ens

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Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting

https://github.com/argoai/argoverse-api

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Argoverse: 3D Tracking and Forecasting with Rich Maps

https://github.com/argoverse/av2-api

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Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

https://github.com/autonomousvision/transfuser

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Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

https://github.com/autovision-cloud/ssl-lanes

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SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving

https://github.com/bchimagine/DeepPredictiveMotionTracking

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Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

https://github.com/cram3r95/mapfe4mp

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Exploring Attention GAN for Vehicle Motion Prediction

https://github.com/csjfwang/forecast-peft

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Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models

https://github.com/dingpx/eai

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Expressive Forecasting of 3D Whole-body Human Motions

https://github.com/dulucas/simlpe

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Back to MLP: A Simple Baseline for Human Motion Prediction

https://github.com/edodema/BestPractices2Body

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Best Practices for 2-Body Pose Forecasting

https://github.com/evendrow/somoformer

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SoMoFormer: Multi-Person Pose Forecasting with Transformers

https://github.com/felixbmuller/sast

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Massively Multi-Person 3D Human Motion Forecasting with Scene Context

https://github.com/fudan-zvg/demo

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DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States

https://github.com/fudan-zvg/realmotion

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Motion Forecasting in Continuous Driving

https://github.com/google-research/causal-agents

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CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships

https://github.com/google/next-prediction

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Peeking into the Future: Predicting Future Person Activities and Locations in Videos

https://github.com/gorkaydemir/FTGN

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Trajectory Forecasting on Temporal Graphs

https://github.com/hkust-aerial-robotics/dsp

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Trajectory Prediction with Graph-based Dual-scale Context Fusion

https://github.com/hkust-aerial-robotics/simpl

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SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving

https://github.com/jchengai/forecast-mae

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Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders

https://github.com/jeffwang987/asap

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Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark

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