In this paper we define the new task of pose-aware shape reconstruction from a single image, and we design architectures of pose-aware shape reconstruction using weak constraint from reprojecting the predicted shape back on to the image with the predicted pose.
In ICCV 2017 [Spotlight]

To be arXived.
Submitted to WACV 2018

In this paper, we propose the concept of Structure from Category (SfC) to reconstruct 3D structure of generic objects solely from images with no shape and motion constraint (i.e. prior-less). First, correspondence determines the location of key points across images of the same object category. Once established, the inverse problem of recovering the 3D structure from the 2D points is solved over an augmented sparse shape-space model.
In 3DV 2016

We propose an efficient alignment algorithm inspired by the Lucas-Kanade Algorithm and the Supervised Descent Method, achieving significant improvement over the two learned with little training data. Superior performance is also achieved in applications including template tracking and facial landmark alignment.
In ECCV 2016