Our method takes as input an unconstrained monocular face image and estimates face attributes -3D pose, geometry, diffuse, specular, roughness and illumination (left). The estimation is self-shadow aware and handles varied illumination conditions. We show several resulting style transfer applications: albedos, illumination and textures transfers from and into face portrait images (right).

No abstract

Abstract-This work is related to the development of a markerless system allowing the tracking of elderly people at home. Microsoft Kinect is a low cost 3D camera adapted to the tracking of human movements. We propose a method for making the fusion of the information provided by several Kinects. The observed space is tesselated into cells forming a 3D occupancy grid. We calculate a probability of occupation for each cell of the grid. From this probability we distinguish whether the cells are occupied or not by a static object (wall) or a mobile object (chair, human being). This categorization is realized in real-time using a simple three states HMM. The proposed method for discriminating between mobile and static objects in a room is the main contribution of this paper. The use of HMMs allows to deal with an aliasing problem since mobile objects result in the same observation as static objects. The approach is evaluated in simulation and in a real environment showing an efficient real-time discrimination between cells occupied by mobile objects and cells occupied by static objects.

The aim of our work is to estimate the camera motion from RGB-D images in a dynamic scene. Most of the existing methods have a poor localization performance in such environments, which makes them inapplicable in real world conditions. In this paper, we propose a new dense visual odometry method that uses RANSAC to cope with dynamic scenes. We show the efficiency and robustness of the proposed method on a large set of experiments in challenging situations and from publicly available benchmark dataset. Additionally, we compare our approach to another state-of-art method based on M-estimator that is used to deal with dynamic scenes. Our method gives similar results on benchmark sequences and better results on our own dataset.