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Pose Prediction

Pose prediction is to predict future poses given a window of previous poses.

Papers

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Showing 51–100 of 163 papers

TitleStatusHype
Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound VideosCode1
Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera DataCode1
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose EstimationCode0
3D Human Pose Regression using Graph Convolutional NetworkCode0
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction PredictionCode0
Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human jointsCode0
Estimating 6D Pose From Localizing Designated Surface KeypointsCode0
Exploring Mutual Cross-Modal Attention for Context-Aware Human Affordance GenerationCode0
A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose PredictionCode0
Multi-Resolution Haar Network: Enhancing human motion prediction via Haar transformCode0
Predcnn: Predictive learning with cascade convolutionsCode0
Protein-Ligand Scoring with Convolutional Neural NetworksCode0
Real-Time Seamless Single Shot 6D Object Pose PredictionCode0
Segmentation-driven 6D Object Pose EstimationCode0
Self-supervised learning of object pose estimation using keypoint predictionCode0
SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep NetworksCode0
Sparse Prototype Network for Explainable Pedestrian Behavior PredictionCode0
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised ApproachCode0
Uni-Mol: A Universal 3D Molecular Representation Learning FrameworkCode0
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition—0
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors—0
Key-Pose Prediction in Cyclic Human Motion—0
Full-Body Motion From a Single Head-Mounted Device: Generating SMPL Poses From Partial Observations—0
Koopman pose predictions for temporally consistent human walking estimations—0
FRAME: Floor-aligned Representation for Avatar Motion from Egocentric Video—0
Forecasting of 3D Whole-body Human Poses with Grasping Objects—0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach—0
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling—0
Forecasting Characteristic 3D Poses of Human Actions—0
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction—0
Long-Term Human Video Generation of Multiple Futures Using Poses—0
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation—0
Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer—0
Marr Revisited: 2D-3D Alignment via Surface Normal Prediction—0
MarsLGPR: Mars Rover Localization with Ground Penetrating Radar—0
Masked GANs for Unsupervised Depth and Pose Prediction with Scale Consistency—0
MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images—0
MathDL: Mathematical deep learning for D3R Grand Challenge 4—0
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges—0
Meta-Auxiliary Learning for Adaptive Human Pose Prediction—0
FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations—0
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions—0
Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations—0
Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking—0
Multi-Graph Convolution Network for Pose Forecasting—0
FlexVDW: A machine learning approach to account for protein flexibility in ligand docking—0
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping—0
Multi-Scale Networks for 3D Human Pose Estimation with Inference Stage Optimization—0
Fine-grained pose prediction, normalization, and recognition—0
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