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Activity Prediction

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

https://github.com/stuarteiffert/MCTS-GRNN

공식 코드

Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search

https://github.com/suhaisheng/difsd

공식 코드

DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving

https://github.com/swc-17/sparsedrive

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SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

https://github.com/syscv/trafficbots

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TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction

https://github.com/tabguigui/fiptr

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FipTR: A Simple yet Effective Transformer Framework for Future Instance Prediction in Autonomous Driving

https://github.com/tda-jyamiti/d-gril

공식 코드

D-GRIL: End-to-End Topological Learning with 2-parameter Persistence

https://github.com/tdavchev/structured-trajectory-prediction

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Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes

https://github.com/tedhuang96/mifwlstm

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Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM

https://github.com/tileb1/motion-prediction-tim

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Motion Prediction Using Temporal Inception Module

https://github.com/tinyvision/IPCodec

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Spatiotemporal Entropy Model is All You Need for Learned Video Compression

https://github.com/tleemann/dpmhp

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Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling

https://github.com/tsinghua-mars-lab/statetransformer

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Large Trajectory Models are Scalable Motion Predictors and Planners

https://github.com/ttlzfhy/D-DPCC

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D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction

https://github.com/tumftm/flexcloud

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FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps

https://github.com/ubisoftinc/Ubisoft-LaForge-Animation-Dataset

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Robust Motion In-betweening

https://github.com/una-dinosauria/human-motion-prediction

공식 코드

On human motion prediction using recurrent neural networks

https://github.com/usaywook/gin

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GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving

https://github.com/v0lta/Complex-gated-recurrent-neural-networks

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Complex Gated Recurrent Neural Networks

https://github.com/valeoai/mf_awta

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Annealed Winner-Takes-All for Motion Forecasting

https://github.com/vietngth/ph-ensemble-gnn

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Persistent Homology-induced Graph Ensembles for Time Series Regressions

https://github.com/vita-epfl/hybrid-feature-fusion

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Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

https://github.com/vita-epfl/multi-transmotion

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Multi-Transmotion: Pre-trained Model for Human Motion Prediction

https://github.com/vita-epfl/pedestrian-transition-dataset

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Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

https://github.com/vita-epfl/tarot

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TAROT: Targeted Data Selection via Optimal Transport

https://github.com/walzimmer/3d-bat

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3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams

https://github.com/wei-mao-2019/HisRepItself

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History Repeats Itself: Human Motion Prediction via Motion Attention

https://github.com/wei-mao-2019/LearnTrajDep

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Learning Trajectory Dependencies for Human Motion Prediction

https://github.com/wei-mao-2019/gsps

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Generating Smooth Pose Sequences for Diverse Human Motion Prediction

https://github.com/wei-mao-2019/wat

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Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction

https://github.com/western-oc2-lab/intrusion-detection-system-using-cnn-and-transfer-learning

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A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

https://github.com/westny/mtp-go

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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

https://github.com/wqi/WIMP

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What-If Motion Prediction for Autonomous Driving

https://github.com/wzzheng/genad

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GenAD: Generative End-to-End Autonomous Driving

https://github.com/wzzheng/gpd

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GPD-1: Generative Pre-training for Driving

https://github.com/xhaughearl/haarmodic

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Multi-Resolution Haar Network: Enhancing human motion prediction via Haar transform

https://github.com/xiandaguo/drive-mllm

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DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large Language Models in Autonomous Driving

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