We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color and depth images captured with an RGB-D camera. We jointly learn the pose from synthetic depth images that are easy to generate, and learn to align these synthetic depth images with the real depth images. We show our approach for the task of 3D hand pose estimation and 3D object pose estimation, both from color images only. Our method achieves performances comparable to state-of-the-art methods on popular benchmark datasets, without requiring any annotations for the color images.
|Number of pages||16|
|Journal||arXiv.org e-Print archive|
|Publication status||Published - 8 Oct 2018|
|Event||14th Asian Conference on Computer Vision: ACCV 2018 - Perth Western Australia, Perth, Australia|
Duration: 4 Dec 2018 → 6 Dec 2018