Pose prediction is to predict future poses given a window of previous poses.
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HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
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.
#10 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.
#3 best model for 6D Pose Estimation using RGB on YCB-Video
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.
#9 best model for 6D Pose Estimation using RGB on LineMOD
Large intra-class variation is the result of changes in multiple object characteristics.