Pose prediction is to predict future poses given a window of previous poses.
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Bottom-up multi-person pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation.
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.
#4 best model for 3D Human Pose Estimation on Geometric Pose Affordance
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.
We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics.
#5 best model for Skeleton Based Action Recognition on Kinetics-Skeleton dataset
A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding.
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.
SOTA for 6D Pose Estimation using RGB on YCB-Video (Accuracy (ADD) metric )
We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches.
In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.
#5 best model for 6D Pose Estimation using RGB on LineMOD (Accuracy metric)
Large intra-class variation is the result of changes in multiple object characteristics.