A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

Overview

Awesome-Human-Pose-Prediction

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A selection of State Of The Art research papers (and code) on human trajectory prediction (forecasting). Papers marked with [W] are workshop papers.

Maintainers: Karttikeya Mangalam

Contributing: Please feel free to pull requests to add new resources or suggest addditions or changes to the list. While proposing a new addition, please keep in mind the following principles:

  • The work has been accepted in a reputable peer reviewed publication venue.
  • An opensource link to the paper pdf is attached (as far as possible).
  • Code for the paper is linked (if made opensource by the authors).

Email: [email protected].{berkeley,stanford).edu

Datasets

  • Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments [Paper]
  • Stanford Drone Dataset (SDD): Learning Social Etiquette: Human Trajectory Understanding in Crowded Scenes [Paper] [Leaderboard]

Papers

As End in Itself

  • From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting [Paper]

  • It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction [Paper]

  • Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data [Paper]

  • Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph [paper]

  • Map-Adaptive Goal-Based Trajectory Prediction [paper]

  • Interaction-Aware Trajectory Prediction based on a 3D Spatio-Temporal Tensor Representation using Convolutional–Recurrent Neural Networks [paper]

  • DROGON: A Trajectory Prediction Model based on Intention-Conditioned Behavior Reasoning [Paper]

  • Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction [Paper]

  • Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians [Paper]

  • Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions [Paper]

  • Social NCE: Contrastive Learning of Socially-aware Motion Representations [Paper]

  • Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach [Paper]

  • Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [Paper]

  • Deep Learning for Vision-based Prediction: A Survey [Paper]

  • Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction Using a Graph Vehicle-Pedestrian Attention Network [Paper]

  • Semantics for Robotic Mapping, Perception and Interaction: A Survey [Paper]

  • Benchmark for Evaluating Pedestrian Action Prediction[Paper]

  • Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking [Paper]

  • Pedestrian Behavior Prediction via Multitask Learning and Categorical Interaction Modeling [Paper]

  • Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction [Paper]

  • Haar Wavelet based Block Autoregressive Flows for Trajectories [Paper]

  • Imitative Planning using Conditional Normalizing Flow [Paper]

  • TNT: Target-driveN Trajectory Prediction [Paper]

  • SimAug: Learning Robust Representations from Simulation for Trajectory Prediction [Paper]

  • SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints [Paper]

  • Social GAN: Socially Acceptable Trajectories With Generative Adversarial Networks [Paper]

  • DESIRE: Distant Future Prediction in Dynamic Scenes With Interacting Agents [Paper]

  • Predicting Whole Body Motion Trajectories using Conditional Neural Movement Primitives [Paper] [W]

  • Anticipating Human Intention for Full-Body Motion Prediction [Paper] [W]

  • Human Motion Prediction With Graph Neural Networks [Paper] [W]

  • Action-Agnostic Human Pose Forecasting [Paper]

  • Human Torso Pose Forecasting in the Real World [Paper]

  • Imitation Learning for Human Pose Prediction [Paper]

  • Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision [Paper]

  • Predicting 3D Human Dynamics from Video [Paper]

  • Recurrent Network Models for Human Dynamics [Paper]

  • Structural-RNN: Deep Learning on Spatio-Temporal Graphs [Paper]

  • Learning Trajectory Dependencies for Human Motion Prediction [Paper]

  • Anticipating many futures: Online human motion prediction and generation for human-robot interaction [Paper]

  • Teaching Robots to Predict Human Motion [Paper]

  • Deep representation learning for human motion prediction and classification [Paper]

  • On human motion prediction using recurrent neural networks [Paper]

  • Few-Shot Human Motion Prediction via Meta-learning [Paper]

  • Efficient convolutional hierarchical autoencoder for human motion prediction [Paper]

  • Learning Human Motion Models for Long-term Predictions [Paper]

  • Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic [Paper]

  • Context-aware Human Motion Prediction [Paper]

  • Adversarial Geometry-Aware Human Motion Prediction [Paper]

  • Convolutional Sequence to Sequence Model for Human Dynamics [Paper]

  • QuaterNet: A Quaternion-based Recurrent Model for Human Motion [Paper]

  • BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN [Paper]

  • Human Motion Modeling using DVGANs [Paper]

  • Human Motion Prediction using Semi-adaptable Neural Networks [Paper]

  • A Neural Temporal Model for Human Motion Prediction [Paper]

  • Modeling Human Motion with Quaternion-based Neural Networks [Paper]

  • Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies [Paper]

  • VRED: A Position-Velocity Recurrent Encoder-Decoder for Human Motion Prediction [Paper]

  • EAN: Error Attenuation Network for Long-term Human Motion Prediction [Paper]

  • Structured Prediction Helps 3D Human Motion Modelling [Paper]

  • Forecasting Human Dynamics from Static Images [Paper]

  • HP-GAN: Probabilistic 3D human motion prediction via GAN [Paper]

  • Learning Latent Representations of 3D Human Pose with Deep Neural Networks [Paper]

  • A Recurrent Variational Autoencoder for Human Motion Synthesis [Paper]

  • Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling [Paper]

  • Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control [Paper]

  • PISEP2: Pseudo Image Sequence Evolution based 3D Pose Prediction [Paper]

  • Human Motion Prediction via Spatio-Temporal Inpainting [Paper]

  • Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction [Paper]

  • Human Pose Forecasting via Deep Markov Models [Paper]

  • Auto-Conditioned Recurrent Networks For Extended Complex Human Motion Synthesis [Paper]

  • Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network [Paper]

As a Subtask

  • The Pose Knows: Video Forecasting by Generating Pose Futures [Paper]
  • I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction [Paper]
  • Language2Pose: Natural Language Grounded Pose Forecasting [Paper]
  • Long-Term Video Generation of Multiple Futures Using Human Poses [Paper]
  • Predicting body movements for person identification under different walking conditions [Paper]
Owner
Karttikeya Manglam
PhD Student in Computer Vision @ BAIR, UC Berkeley.
Karttikeya Manglam
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