Pose Prediction
57 papers with code • 3 benchmarks • 8 datasets
Pose prediction is to predict future poses given a window of previous poses.
Datasets
Most implemented papers
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
Estimating 6D Pose From Localizing Designated Surface Keypoints
In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.
Real-Time Seamless Single Shot 6D Object Pose Prediction
For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches when they are all used without post-processing.
Segmentation-driven 6D Object Pose Estimation
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm.
HOnnotate: A method for 3D Annotation of Hand and Object Poses
This dataset is currently made of 77, 558 frames, 68 sequences, 10 persons, and 10 objects.
Protein-Ligand Scoring with Convolutional Neural Networks
A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding.
3D Hand Shape and Pose from Images in the Wild
We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild.
Unsupervised Part-Based Disentangling of Object Shape and Appearance
Large intra-class variation is the result of changes in multiple object characteristics.
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning.