Pose Prediction

32 papers with code • 3 benchmarks • 7 datasets

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

Most implemented papers

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

HRNet/Higher-HRNet-Human-Pose-Estimation CVPR 2020

HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.

Estimating 6D Pose From Localizing Designated Surface Keypoints

sjtuytc/betapose 4 Dec 2018

In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach

xingyizhou/pose-hg-3d ICCV 2017

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.

Real-Time Seamless Single Shot 6D Object Pose Prediction

Microsoft/singleshotpose CVPR 2018

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

cvlab-epfl/segmentation-driven-pose CVPR 2019

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

shreyashampali/ho3d CVPR 2020

This dataset is currently made of 77, 558 frames, 68 sequences, 10 persons, and 10 objects.

Protein-Ligand Scoring with Convolutional Neural Networks

gnina/gnina 8 Dec 2016

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

yihui-he/epipolar-transformers CVPR 2019

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

NVIDIA/UnsupervisedLandmarkLearning CVPR 2019

Large intra-class variation is the result of changes in multiple object characteristics.

SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks

val-iisc/sketch-parse 5 Sep 2017

We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches.