… kinematic object model into the deep neutral network learning for general articulated object
pose … we show that integrating a kinematic object model into deep learning achieves state-of-…

… In this paper, we propose a deep kinematics analysis framework for monocular 3D pose
estimation. By explicitly incorporating kinematics regularization into deep models, we achieves …

We propose a novel efficient and lightweight model for human pose estimation from a single
image. Our model is designed to achieve competitive results at a fraction of the number of …

… to produce a motion, while kinematics focuses only on the … kinematics, which determines
the final position of the robot end as a function of the given inputs, and the inverse kinematics, …

… of deep learning enable the measurement of kinematic coefficients directly from human pose
… swimming pool by means of a deep neural network architecture for key-pose regression. [24…

… Compared with previous hand pose regression methods that define the kinematics
structure explicitly [4], [17], our method learns the underlying pattern implicitly by learning the …

… In this work, we propose a method for 3-D pose estimation of flexible surgical instruments.
The proposed approach is self-supervised and trains a model to regress the kinematic …

… Overview of our approach for hand shape and pose regression. A depth input (I D ) is given
to the convolutional neural network (CNN), which provides a feature map for four distinct …

… regression method for hand pose … regression of 2D joint detections helps 3D regression
and how different strategies to fuse 2D joint detections and 3D regressions impact the final pose …

… pose regression method [22] and feature points regression of human face [6], in this paper,
we assume that the more regression/… have, the more accurate human pose we can get. This …