Video Interpolation using Optical Flow and Laplacian Smoothness

Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the local smoothness. Similar to Li et al., our approach adopts a mesh to the image with a resolution up to one vertex per pixel and uses angle constraints to ensure sensible local deformations between image pairs. The Laplacian Mesh constraints are expressed wholly inside the optical flow optimization, and can be applied in a straightforward manner to a wide range of image tracking and registration problems. We evaluate our approach by testing on several benchmark datasets, including the Middlebury and Garg et al. datasets. In addition, we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data

Active nonrigid ICP algorithm

© 2015 IEEE.The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 15-20 years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region

Enhanced 3D Capture for Room-sized Dynamic Scenes with Commodity Depth Cameras

3D reconstruction of dynamic scenes can find many applications in areas such as virtual/augmented reality, 3D telepresence and 3D animation, while it is challenging to achieve a complete and high quality reconstruction due to the sensor noise and occlusions in the scene. This dissertation demonstrates our efforts toward building a 3D capture system for room-sized dynamic environments. A key observation is that reconstruction insufficiency (e.g., incompleteness and noise) can be mitigated by accumulating data from multiple frames. In dynamic environments, dropouts in 3D reconstruction generally do not consistently appear in the same locations. Thus, accumulation of the captured 3D data over time can fill in the missing fragments. Reconstruction noise is reduced as well. The first piece of the system builds 3D models for room-scale static scenes with one hand-held depth sensor, where we use plane features, in addition to image salient points, for robust pairwise matching and bundle adjustment over the whole data sequence. In the second piece of the system, we designed a robust non-rigid matching algorithm that considers both dense point alignment and color similarity, so that the data sequence for a continuously deforming object captured by multiple depth sensors can be aligned together and fused into a high quality 3D model. We further extend this work for deformable object scanning with a single depth sensor. To deal with the drift problem, we designed a dense nonrigid bundle adjustment algorithm to simultaneously optimize for the final mesh and the deformation parameters of every frame. Finally, we integrate static scanning and nonrigid matching into a reconstruction system for room-sized dynamic environments, where we prescan the static parts of the scene and perform data accumulation for dynamic parts. Both rigid and nonrigid motions of objects are tracked in a unified framework, and close contacts between objects are also handled. The dissertation demonstrates significant improvements for dense reconstruction over state-of-the-art. Our plane-based scanning system for indoor environments delivers reliable reconstruction for challenging situations, such as lack of both visual and geometrical salient features. Our nonrigid alignment algorithm enables data fusion for deforming objects and thus achieves dramatically enhanced reconstruction. Our novel bundle adjustment algorithm handles dense input partial scans with nonrigid motion and outputs dense reconstruction with comparably high quality as the static scanning algorithm (e.g., KinectFusion). Finally, we demonstrate enhanced reconstruction results for room-sized dynamic environments by integrating the above techniques, which significantly advances state-of-the-art.Doctor of Philosoph