ABSTRACT
We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.
Subject(s)
Human Body , Algorithms , Computer Graphics , HumansABSTRACT
We propose "StereoPasting," an efficient method for depth-consistent stereoscopic composition, in which a source 2D image is interactively blended into a target stereoscopic image. As we paint "disparity" on a 2D image, the disparity map of the selected region is gradually produced by edge-aware diffusion, and then blended with that of the target stereoscopic image. By considering constraints of the expected disparities and perspective scaling, the 2D object is warped to generate an image pair, which is then blended into the target image pair to get the composition result. The warping is formulated as an energy minimization, which could be solved in real time. We also present an interactive composition system, in which users can edit the disparity maps of 2D images by strokes, while viewing the composition results instantly. Experiments show that our method is intuitive and efficient for interactive stereoscopic composition. A lot of applications demonstrate the versatility of our method.