Abstract: Introduced here is an approach to allowing a computer-implemented model to learn its own ordering scheme by applying an appropriate loss function during training. More generally, the present disclosure pertains to computer programs and associated computer-implemented techniques for estimating pose of a living body through simultaneous analysis of multiple visualizations. For example, joint type heatmaps—where a given heatmap includes every visible joint of the corresponding type across all visible persons—can be combined with joint person heatmaps—where a given heatmap includes every visible joint of the corresponding person—to better understand the relationship between joints visible in a digital image and people included in the digital image.

Abstract: Introduced here is an approach to allowing a computer-implemented model to learn its own ordering scheme by applying an appropriate loss function during training. More generally, the present disclosure pertains to computer programs and associated computer-implemented techniques for estimating pose of a living body through simultaneous analysis of multiple visualizations. For example, joint type heatmaps—where a given heatmap includes every visible joint of the corresponding type across all visible persons—can be combined with joint person heatmaps—where a given heatmap includes every visible joint of the corresponding person—to better understand the relationship between joints visible in a digital image and people included in the digital image.

Abstract: Systems and methods are provided for estimating the 3D joint location of skeleton joints from an image segment of an object and a 2D joint heatmaps comprising 2D locations of skeleton joints on the image segment. This includes applying the image segment and 2D joint heatmaps to a convolutional neural network containing at least one 3D convolutional layer block, wherein the 2D resolution is reduced at each 3D convolutional layer and the depth resolution is expanded to produce an estimated depth for each joint. Combining the 2D location of each kind of joint with the estimated depth of the kind of joint generates an estimated 3D joint position of the skeleton joint.

Abstract: Systems and methods are provided for estimating the 3D joint location of skeleton joints from an image segment of an object and a 2D joint heatmaps comprising 2D locations of skeleton joints on the image segment. This includes applying the image segment and 2D joint heatmaps to a convolutional neural network containing at least one 3D convolutional layer block, wherein the 2D resolution is reduced at each 3D convolutional layer and the depth resolution is expanded to produce an estimated depth for each joint. Combining the 2D location of each kind of joint with the estimated depth of the kind of joint generates an estimated 3D joint position of the skeleton joint.

Abstract: An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of a first object and a second object. The apparatus further includes a memory storage unit to store the raw data. In addition, the apparatus includes a neural network engine to receive the raw data. The neural network engine is to generate a segmentation map and a boundary map. The apparatus also includes a post-processing engine to identify the first object and the second object based on the segmentation map and the boundary map.

Abstract: Systems and methods are provided for estimating the 3D joint location of skeleton joints from an image segment of an object and a 2D joint heatmaps comprising 2D locations of skeleton joints on the image segment. This includes applying the image segment and 2D joint heatmaps to a convolutional neural network containing at least one 3D convolutional layer block, wherein the 2D resolution is reduced at each 3D convolutional layer and the depth resolution is expanded to produce an estimated depth for each joint. Combining the 2D location of each kind of joint with the estimated depth of the kind of joint generates an estimated 3D joint position of the skeleton joint.

Abstract: Systems and methods are provided for estimating the 3D joint location of skeleton joints from an image segment of an object and a 2D joint heatmaps comprising 2D locations of skeleton joints on the image segment. This includes applying the image segment and 2D joint heatmaps to a convolutional neural network containing at least one 3D convolutional layer block, wherein the 2D resolution is reduced at each 3D convolutional layer and the depth resolution is expanded to produce an estimated depth for each joint. Combining the 2D location of each kind of joint with the estimated depth of the kind of joint generates an estimated 3D joint position of the skeleton joint.

Abstract: Systems and methods are provided for estimating the 3D joint location of skeleton joints from an image segment of an object and a 2D joint heatmaps comprising 2D locations of skeleton joints on the image segment. This includes applying the image segment and 2D joint heatmaps to a convolutional neural network containing at least one 3D convolutional layer block, wherein the 2D resolution is reduced at each 3D convolutional layer and the depth resolution is expanded to produce an estimated depth for each joint. Combining the 2D location of each kind of joint with the estimated depth of the kind of joint generates an estimated 3D joint position of the skeleton joint.