{IsMo-GAN}: {A}dversarial Learning for Monocular Non-Rigid {3D} Reconstruction

The majority of the existing methods for non-rigid 3D surface regression from monocular 2D images require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates. In this work, we present the Isometry-Aware Monocular Generative Adversarial Network (IsMo-GAN) - an approach for direct 3D reconstruction from a single image, trained for the deformation model in an adversarial manner on a light-weight synthetic dataset. IsMo-GAN reconstructs surfaces from real images under varying illumination, camera poses, textures and shading at over 250 Hz. In multiple experiments, it consistently outperforms several approaches in the reconstruction accuracy, runtime, generalisation to unknown surfaces and robustness to occlusions. In comparison to the state-of-the-art, we reduce the reconstruction error by 10-30% including the textureless case and our surfaces evince fewer artefacts qualitatively

Inverse rendering for scene reconstruction in general environments

Demand for high-quality 3D content has been exploding recently, owing to the advances in 3D displays and 3D printing. However, due to insufficient 3D content, the potential of 3D display and printing technology has not been realized to its full extent. Techniques for capturing the real world, which are able to generate 3D models from captured images or videos, are a hot research topic in computer graphics and computer vision. Despite significant progress, many methods are still highly constrained and require lots of prerequisites to succeed. Marker-less performance capture is one such dynamic scene reconstruction technique that is still confined to studio environments. The requirements involved, such as the need for a multi-view camera setup, specially engineered lighting or green-screen backgrounds, prevent these methods from being widely used by the film industry or even by ordinary consumers. In the area of scene reconstruction from images or videos, this thesis proposes new techniques that succeed in general environments, even using as few as two cameras. Contributions are made in terms of reducing the constraints of marker-less performance capture on lighting, background and the required number of cameras. The primary theoretical contribution lies in the investigation of light transport mechanisms for high-quality 3D reconstruction in general environments. Several steps are taken to approach the goal of scene reconstruction in general environments. At first, the concept of employing inverse rendering for scene reconstruction is demonstrated on static scenes, where a high-quality multi-view 3D reconstruction method under general unknown illumination is developed. Then, this concept is extended to dynamic scene reconstruction from multi-view video, where detailed 3D models of dynamic scenes can be captured under general and even varying lighting, and in front of a general scene background without a green screen. Finally, efforts are made to reduce the number of cameras employed. New performance capture methods using as few as two cameras are proposed to capture high-quality 3D geometry in general environments, even outdoors.Die Nachfrage nach qualitativ hochwertigen 3D Modellen ist in letzter Zeit, bedingt durch den technologischen Fortschritt bei 3D-Wieder-gabegeräten und -Druckern, stark angestiegen. Allerdings konnten diese Technologien wegen mangelnder Inhalte nicht ihr volles Potential entwickeln. Methoden zur Erfassung der realen Welt, welche 3D-Modelle aus Bildern oder Videos generieren, sind daher ein brandaktuelles Forschungsthema im Bereich Computergrafik und Bildverstehen. Trotz erheblichen Fortschritts in dieser Richtung sind viele Methoden noch stark eingeschränkt und benötigen viele Voraussetzungen um erfolgreich zu sein. Markerloses Performance Capturing ist ein solches Verfahren, das dynamische Szenen rekonstruiert, aber noch auf Studio-Umgebungen beschränkt ist. Die spezifischen Anforderung solcher Verfahren, wie zum Beispiel einen Mehrkameraaufbau, maßgeschneiderte, kontrollierte Beleuchtung oder Greenscreen-Hintergründe verhindern die Verbreitung dieser Verfahren in der Filmindustrie und besonders bei Endbenutzern. Im Bereich der Szenenrekonstruktion aus Bildern oder Videos schlägt diese Dissertation neue Methoden vor, welche in beliebigen Umgebungen und auch mit nur wenigen (zwei) Kameras funktionieren. Dazu werden Schritte unternommen, um die Einschränkungen bisheriger Verfahren des markerlosen Performance Capturings im Hinblick auf Beleuchtung, Hintergründe und die erforderliche Anzahl von Kameras zu verringern. Der wichtigste theoretische Beitrag liegt in der Untersuchung von Licht-Transportmechanismen für hochwertige 3D-Rekonstruktionen in beliebigen Umgebungen. Dabei werden mehrere Schritte unternommen, um das Ziel der Szenenrekonstruktion in beliebigen Umgebungen anzugehen. Zunächst wird die Anwendung von inversem Rendering auf die Rekonstruktion von statischen Szenen dargelegt, indem ein hochwertiges 3D-Rekonstruktionsverfahren aus Mehransichtsaufnahmen unter beliebiger, unbekannter Beleuchtung entwickelt wird. Dann wird dieses Konzept auf die dynamische Szenenrekonstruktion basierend auf Mehransichtsvideos erweitert, wobei detaillierte 3D-Modelle von dynamischen Szenen unter beliebiger und auch veränderlicher Beleuchtung vor einem allgemeinen Hintergrund ohne Greenscreen erfasst werden. Schließlich werden Anstrengungen unternommen die Anzahl der eingesetzten Kameras zu reduzieren. Dazu werden neue Verfahren des Performance Capturings, unter Verwendung von lediglich zwei Kameras vorgeschlagen, um hochwertige 3D-Geometrie im beliebigen Umgebungen, sowie im Freien, zu erfassen

Depth Enhancement and Surface Reconstruction with RGB/D Sequence

Surface reconstruction and 3D modeling is a challenging task, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. It is fundamental to many applications such as robot navigation, animation and scene understanding, industrial control and medical diagnosis. In this dissertation, I take advantage of the consumer depth sensors for surface reconstruction. Considering its limited performance on capturing detailed surface geometry, a depth enhancement approach is proposed in the first place to recovery small and rich geometric details with captured depth and color sequence. In addition to enhancing its spatial resolution, I present a hybrid camera to improve the temporal resolution of consumer depth sensor and propose an optimization framework to capture high speed motion and generate high speed depth streams. Given the partial scans from the depth sensor, we also develop a novel fusion approach to build up complete and watertight human models with a template guided registration method. Finally, the problem of surface reconstruction for non-Lambertian objects, on which the current depth sensor fails, is addressed by exploiting multi-view images captured with a hand-held color camera and we propose a visual hull based approach to recovery the 3D model

Structure from Recurrent Motion: From Rigidity to Recurrency

This paper proposes a new method for Non-Rigid Structure-from-Motion (NRSfM) from a long monocular video sequence observing a non-rigid object performing recurrent and possibly repetitive dynamic action. Departing from the traditional idea of using linear low-order or lowrank shape model for the task of NRSfM, our method exploits the property of shape recurrency (i.e., many deforming shapes tend to repeat themselves in time). We show that recurrency is in fact a generalized rigidity. Based on this, we reduce NRSfM problems to rigid ones provided that certain recurrency condition is satisfied. Given such a reduction, standard rigid-SfM techniques are directly applicable (without any change) to the reconstruction of non-rigid dynamic shapes. To implement this idea as a practical approach, this paper develops efficient algorithms for automatic recurrency detection, as well as camera view clustering via a rigidity-check. Experiments on both simulated sequences and real data demonstrate the effectiveness of the method. Since this paper offers a novel perspective on rethinking structure-from-motion, we hope it will inspire other new problems in the field.Comment: To appear in CVPR 201

Subspace Representations for Robust Face and Facial Expression Recognition

Analyzing human faces and modeling their variations have always been of interest to the computer vision community. Face analysis based on 2D intensity images is a challenging problem, complicated by variations in pose, lighting, blur, and non-rigid facial deformations due to facial expressions. Among the different sources of variation, facial expressions are of interest as important channels of non-verbal communication. Facial expression analysis is also affected by changes in view-point and inter-subject variations in performing different expressions. This dissertation makes an attempt to address some of the challenges involved in developing robust algorithms for face and facial expression recognition by exploiting the idea of proper subspace representations for data. Variations in the visual appearance of an object mostly arise due to changes in illumination and pose. So we first present a video-based sequential algorithm for estimating the face albedo as an illumination-insensitive signature for face recognition. We show that by knowing/estimating the pose of the face at each frame of a sequence, the albedo can be efficiently estimated using a Kalman filter. Then we extend this to the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through an efficient Bayesian inference method performed using a Rao-Blackwellized particle filter. Since understanding the effects of blur, especially motion blur, is an important problem in unconstrained visual analysis, we then propose a blur-robust recognition algorithm for faces with spatially varying blur. We model a blurred face as a weighted average of geometrically transformed instances of its clean face. We then build a matrix, for each gallery face, whose column space spans the space of all the motion blurred images obtained from the clean face. This matrix representation is then used to define a proper objective function and perform blur-robust face recognition. To develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera. To this end, we build models for expressions on the affine shape-space (Grassmann manifold), as an approximation to the projective shape-space, by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. This representation enables us to perform various expression analysis and recognition algorithms without the need for pose normalization as a preprocessing step. There is a large degree of inter-subject variations in performing various expressions. This poses an important challenge on developing robust facial expression recognition algorithms. To address this challenge, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of action units (AUs). First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. We propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component, which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm, which benefits from the idea of sparsity and morphological diversity. The DCS algorithm uses the data-driven dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition

3D facial shape estimation from a single image under arbitrary pose and illumination.

Humans have the uncanny ability to perceive the world in three dimensions (3D), otherwise known as depth perception. The amazing thing about this ability to determine distances is that it depends only on a simple two-dimensional (2D) image in the retina. It is an interesting problem to explain and mimic this phenomenon of getting a three-dimensional perception of a scene from a flat 2D image of the retina. The main objective of this dissertation is the computational aspect of this human ability to reconstruct the world in 3D using only 2D images from the retina. Specifically, the goal of this work is to recover 3D facial shape information from a single image of unknown pose and illumination. Prior shape and texture models from real data, which are metric in nature, are incorporated into the 3D shape recovery framework. The output recovered shape, likewise, is metric, unlike previous shape-from-shading (SFS) approaches that only provide relative shape. This work starts first with the simpler case of general illumination and fixed frontal pose. Three optimization approaches were developed to solve this 3D shape recovery problem, starting from a brute-force iterative approach to a computationally efficient regression method (Method II-PCR), where the classical shape-from-shading equation is cast as a regression framework. Results show that the output of the regression-like approach is faster in timing and similar in error metrics when compared to its iterative counterpart. The best of the three algorithms above, Method II-PCR, is compared to its two predecessors, namely: (a) Castelan et al. [1] and (b) Ahmed et al. [2]. Experimental results show that the proposed method (Method II-PCR) is superior in all aspects compared to the previous state-of-the-art. Robust statistics was also incorporated into the shape recovery framework to deal with noise and occlusion. Using multiple-view geometry concepts [3], the fixed frontal pose was relaxed to arbitrary pose. The best of the three algorithms above, Method II-PCR, once again is used as the primary 3D shape recovery method. Results show that the pose-invariant 3D shape recovery version (for input with pose) has similar error values compared to the frontal-pose version (for input with frontal pose), for input images of the same subject. Sensitivity experiments indicate that the proposed method is, indeed, invariant to pose, at least for the pan angle range of (-50° to 50°). The next major part of this work is the development of 3D facial shape recovery methods, given only the input 2D shape information, instead of both texture and 2D shape. The simpler case of output 3D sparse shapes was dealt with, initially. The proposed method, which also use a regression-based optimization approach, was compared with state-of-the art algorithms, showing decent performance. There were five conclusions that drawn from the sparse experiments, namely, the proposed approach: (a) is competitive due to its linear and non-iterative nature, (b) does not need explicit training, as opposed to [4], (c) has comparable results to [4], at a shorter computational time, (d) better in all aspects than Zhang and Samaras [5], and (e) has the limitation, together with [4] and [5], in terms of the need to manually annotate the input 2D feature points. The proposed method was then extended to output 3D dense shapes simply by replacing the sparse model with its dense equivalent, in the regression framework inside the 3D face recovery approach. The numerical values of the mean height and surface orientation error indicate that even if shading information is unavailable, a decent 3D dense reconstruction is still possible

Physically based geometry and reflectance recovery from images

An image is a projection of the three-dimensional world taken at an instance in space and time. Its formation involves a complex interplay between geometry, illumination and material properties of objects in the scene. Given image data and knowledge of some scene properties, the recovery of the remaining components can be cast as a set of physically based inverse problems. This thesis investigates three inverse problems on the recovery of scene properties and discusses how we can develop appropriate physical constraints and build them into effective algorithms. Firstly, we study the problem of geometry recovery from a single image with repeated texture. Our technique leverages the PatchMatch algorithm to detect and match repeated patterns undergoing geometric transformations. This allows effective enforcement of translational symmetry constraint in the recovery of texture lattice. Secondly, we study the problem of computational relighting using RGB-D data, where the depth data is acquired through a Kinect sensor and is often noisy. We show how the inclusion of noisy depth input helps to resolve ambiguities in the recovery of shape and reflectance in the inverse rendering problem. Our results show that the complementary nature of RGB and depth is highly beneficial for a practical relighting system. Lastly, in the third problem, we exploit the use of geometric constraints relating two views, to address a challenging problem in Internet image matching. Our solution is robust to geometric and photometric distortions over wide baselines. It also accommodates repeated structures that are commonly found in our modern environment. Building on the image correspondence, we also investigate the use of color transfer as an additional global constraint in relating Internet images. It shows promising results in obtaining more accurate and denser correspondence