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motion prediction
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xhaughearl/haarmodic
공식 코드Multi-Resolution Haar Network: Enhancing human motion prediction via Haar transform
https://github.com/xiandaguo/drive-mllm
공식 코드https://github.com/xiaogangpeng/socialtgcn
공식 코드The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction
https://github.com/xincoder/GRIP
공식 코드GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/xrenaa/safety-aware-motion-prediction
공식 코드Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
https://github.com/xushenlz/parksim
공식 코드https://github.com/xxh43/ukmd
공식 코드Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections
https://github.com/y-zheng18/gimo
공식 코드https://github.com/yandex-research/shifts
공식 코드Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
https://github.com/yfzhang/vehicle-motion-forecasting
공식 코드https://github.com/youngzhou1999/smartpretrain
공식 코드SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
https://github.com/youshaamurhij/ofmpnet
공식 코드OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment
https://github.com/yuhao-nie/stanford-solar-forecasting-dataset
공식 코드https://github.com/yuxiaochen1103/Hi-TRS
공식 코드Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
https://github.com/zhangyp15/beverse
공식 코드BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
https://github.com/zhaoxing2022/mmn-vsod
공식 코드Motion-aware Memory Network for Fast Video Salient Object Detection
https://github.com/zhejz/hptr
공식 코드Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
https://github.com/zhejz/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/zshn25/pc4consistentdepth
공식 코드Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
https://github.com/zympsyche/bevworld
공식 코드BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tracktor
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tracktor%2B%2B
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tracktor%2B%2B
https://github.com/AlbertFeng-0405/SceneTransformer-pytorch
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/Djmcflush/Funky-Movement
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
https://github.com/FunmiKesa/JLA
Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting
https://github.com/HoganZhang/mot_neural_solver
https://github.com/Jacoo-Zhao/3D-Pose-Based-Feedback-For-Physical-Exercises
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/JunweiLiang/social-distancing-prediction
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
https://github.com/LKLQQ/tracktor
https://github.com/MindCode-4/code-12/tree/main/multimodal-trajectory
https://github.com/MindSpore-paper-code-2/code3/tree/main/tracktor%2B%2B
https://github.com/MkuuWaUjinga/Self-Supervised-Learning-for-Tracktor
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.