Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.
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In this paper, we present a novel approach to estimate 3D hand joint locations from 2D depth images.
Once the model is successfully fitted to input RGB images, its meshes i. e. shapes and articulations, are realistic, and we augment view-points on top of estimated dense hand poses.
We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs.
This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image.
We present a self-supervision method for 3D hand pose estimation from depth maps.
In addition to the pose estimation task, the voting-based scheme can also provide point cloud segmentation result without ground-truth for segmentation.
Tremendous headway has been made in the field of 3D hand pose estimation but the 3D depth cameras are usually inaccessible.
To use our method, we build a model, in which we design a particular SFR and its correlative DD which divided the 3D joint coordinates into two parts, plane coordinates and depth coordinates and use two modules named Plane Regression (PR) and Depth Regression (DR) to deal with them respectively.
Hand pose estimation from the monocular 2D image is challenging due to the variation in lighting, appearance, and background.