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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/bshfang/self-supervised-motion

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Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals

https://github.com/cagatayyildiz/ODE2VAE

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ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks

https://github.com/chengxuduan/advhmp

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Physics-constrained Attack against Convolution-based Human Motion Prediction

https://github.com/cing2/limtr

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LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration

https://github.com/continental/empirical-bayes-analysis-of-object-trajectory

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An Empirical Bayes Analysis of Object Trajectory Representation Models

https://github.com/cr7anand/neural_temporal_models

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A Neural Temporal Model for Human Motion Prediction

https://github.com/cram3r95/mapfe4mp

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Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction

https://github.com/cschoeller/constant_velocity_pedestrian_motion

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What the Constant Velocity Model Can Teach Us About Pedestrian 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/cv-stuttgart/detectiondefenses

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Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow

https://github.com/cvlab-epfl/mvflow

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Multi-view Tracking Using Weakly Supervised Human Motion Prediction

https://github.com/david9dragon9/air

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$AIR^2$ for Interaction Prediction

https://github.com/decisionforce/mmTransformer

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Multimodal Motion Prediction with Stacked Transformers

https://github.com/dmytrozabolotnii/autoware_mini

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Pedestrian motion prediction evaluation for urban autonomous driving

https://github.com/droliven/diverse_sampling

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Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space

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/edshieh/motionprediction

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A Mixture of Experts Approach to 3D Human Motion Prediction

https://github.com/edwardleelpz/powerbev

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PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View

https://github.com/eth-ait/motion-transformer

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A Spatio-temporal Transformer for 3D Human Motion Prediction

https://github.com/evendrow/somoformer

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SoMoFormer: Multi-Person Pose Forecasting with Transformers

https://github.com/facebookresearch/co-tracker

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CoTracker: It is Better to Track Together

https://github.com/fanglaosi/skeleton-in-context

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Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning

https://github.com/fcntes/indoordepth

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Deeper into Self-Supervised Monocular Indoor Depth Estimation

https://github.com/fengzicai/awesomewmad

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A Survey of World Models for Autonomous Driving

https://github.com/fengzicai/wmad-benchmarks

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A Survey of World Models for Autonomous Driving

https://github.com/fordcvresearch/fir-trajectory-prediction

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FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving

https://github.com/google-research/google-research/tree/master/vct

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VCT: A Video Compression Transformer

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/guoweixu368/sld-hmp

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Learning Semantic Latent Directions for Accurate and Controllable Human Motion Prediction

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/hongsong-wang/PVRNN

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PVRED: A Position-Velocity Recurrent Encoder-Decoder for Human Motion Prediction

https://github.com/hvision-nku/storydiffusion

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StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation

https://github.com/hwj20/apex_exp

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APEX: Empowering LLMs with Physics-Based Task Planning for Real-time Insight

https://github.com/hyqlat/ATACB

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Stochastic Human Motion Prediction with Memory of Action Transition and Action Characteristic

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