Pose Prediction
57 papers with code • 3 benchmarks • 8 datasets
Pose prediction is to predict future poses given a window of previous poses.
Benchmarks
These leaderboards are used to track progress in Pose Prediction
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Datasets
NTU RGB+D
G3D
Expi
SportsPose
Drunkard's Dataset
InfiniteRep
Latest papers with no code
Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement
In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion.
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction
We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the relative camera poses in ~1. 3 seconds on a single A100 GPU.
ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking
Predicting the docking between proteins and ligands is a crucial and challenging task for drug discovery.
HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery
We propose HydraScreen, a deep-learning approach that aims to provide a framework for more robust machine-learning-accelerated drug discovery.
Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement
In these methods, only a subset of the input dataset is needed to train neural networks for the estimation of poses and conformations.
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction
Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploit human motion patterns.
Equivariant Single View Pose Prediction Via Induced and Restricted Representations
We show that an algorithm that learns a three-dimensional representation of the world from two dimensional images must satisfy certain geometric consistency properties which we formulate as SO(2)-equivariance constraints.
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments
In this work, we propose recurrent fusion networks to optimally align absolute and relative pose predictions to improve the absolute pose prediction.
Meta-Auxiliary Learning for Adaptive Human Pose Prediction
Predicting high-fidelity future human poses, from a historically observed sequence, is decisive for intelligent robots to interact with humans.
Multi-Graph Convolution Network for Pose Forecasting
The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks.
