MonoPerfCap: Human Performance Capture from Monocular Video

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201

Joint Material and Illumination Estimation from Photo Sets in the Wild

Faithful manipulation of shape, material, and illumination in 2D Internet images would greatly benefit from a reliable factorization of appearance into material (i.e., diffuse and specular) and illumination (i.e., environment maps). On the one hand, current methods that produce very high fidelity results, typically require controlled settings, expensive devices, or significant manual effort. To the other hand, methods that are automatic and work on 'in the wild' Internet images, often extract only low-frequency lighting or diffuse materials. In this work, we propose to make use of a set of photographs in order to jointly estimate the non-diffuse materials and sharp lighting in an uncontrolled setting. Our key observation is that seeing multiple instances of the same material under different illumination (i.e., environment), and different materials under the same illumination provide valuable constraints that can be exploited to yield a high-quality solution (i.e., specular materials and environment illumination) for all the observed materials and environments. Similar constraints also arise when observing multiple materials in a single environment, or a single material across multiple environments. The core of this approach is an optimization procedure that uses two neural networks that are trained on synthetic images to predict good gradients in parametric space given observation of reflected light. We evaluate our method on a range of synthetic and real examples to generate high-quality estimates, qualitatively compare our results against state-of-the-art alternatives via a user study, and demonstrate photo-consistent image manipulation that is otherwise very challenging to achieve

What Is Around The Camera?

How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped and far-from-perfect mirror. An additional challenge is that its appearance confounds the light coming from the environment with the unknown materials it is made of. We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals. The proposed method allows us to jointly model the statistics of environments and material properties. We train our system from synthesized training data, but demonstrate its applicability to real-world data. Interestingly, our analysis shows that the information obtained from objects made out of multiple materials often is complementary and leads to better performance.Comment: Accepted to ICCV. Project: http://homes.esat.kuleuven.be/~sgeorgou/multinatillum

Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions

LiveCap: Real-time Human Performance Capture from Monocular Video

We present the first real-time human performance capture approach that reconstructs dense, space-time coherent deforming geometry of entire humans in general everyday clothing from just a single RGB video. We propose a novel two-stage analysis-by-synthesis optimization whose formulation and implementation are designed for high performance. In the first stage, a skinned template model is jointly fitted to background subtracted input video, 2D and 3D skeleton joint positions found using a deep neural network, and a set of sparse facial landmark detections. In the second stage, dense non-rigid 3D deformations of skin and even loose apparel are captured based on a novel real-time capable algorithm for non-rigid tracking using dense photometric and silhouette constraints. Our novel energy formulation leverages automatically identified material regions on the template to model the differing non-rigid deformation behavior of skin and apparel. The two resulting non-linear optimization problems per-frame are solved with specially-tailored data-parallel Gauss-Newton solvers. In order to achieve real-time performance of over 25Hz, we design a pipelined parallel architecture using the CPU and two commodity GPUs. Our method is the first real-time monocular approach for full-body performance capture. Our method yields comparable accuracy with off-line performance capture techniques, while being orders of magnitude faster

Learning to Use Illumination Gradients as an Unambiguous Cue to Three Dimensional Shape

The luminance and colour gradients across an image are the result of complex interactions between object shape, material and illumination. Using such variations to infer object shape or surface colour is therefore a difficult problem for the visual system. We know that changes to the shape of an object can affect its perceived colour, and that shading gradients confer a sense of shape. Here we investigate if the visual system is able to effectively utilise these gradients as a cue to shape perception, even when additional cues are not available. We tested shape perception of a folded card object that contained illumination gradients in the form of shading and more subtle effects such as inter-reflections. Our results suggest that observers are able to use the gradients to make consistent shape judgements. In order to do this, observers must be given the opportunity to learn suitable assumptions about the lighting and scene. Using a variety of different training conditions, we demonstrate that learning can occur quickly and requires only coarse information. We also establish that learning does not deliver a trivial mapping between gradient and shape; rather learning leads to the acquisition of assumptions about lighting and scene parameters that subsequently allow for gradients to be used as a shape cue. The perceived shape is shown to be consistent for convex and concave versions of the object that exhibit very different shading, and also similar to that delivered by outline, a largely unrelated cue to shape. Overall our results indicate that, although gradients are less reliable than some other cues, the relationship between gradients and shape can be quickly assessed and the gradients therefore used effectively as a visual shape cue

Recovering refined surface normals for relighting clothing in dynamic scenes

In this paper we present a method to relight captured 3D video sequences of non-rigid, dynamic scenes, such as clothing of real actors, reconstructed from multiple view video. A view-dependent approach is introduced to refine an initial coarse surface reconstruction using shape-from-shading to estimate detailed surface normals. The prior surface approximation is used to constrain the simultaneous estimation of surface normals and scene illumination, under the assumption of Lambertian surface reflectance. This approach enables detailed surface normals of a moving non-rigid object to be estimated from a single image frame. Refined normal estimates from multiple views are integrated into a single surface normal map. This approach allows highly non-rigid surfaces, such as creases in clothing, to be relit whilst preserving the detailed dynamics observed in video

Learning to Generate 3D Training Data

Human-level visual 3D perception ability has long been pursued by researchers in computer vision, computer graphics, and robotics. Recent years have seen an emerging line of works using synthetic images to train deep networks for single image 3D perception. Synthetic images rendered by graphics engines are a promising source for training deep neural networks because it comes with perfect 3D ground truth for free. However, the 3D shapes and scenes to be rendered are largely made manual. Besides, it is challenging to ensure that synthetic images collected this way can help train a deep network to perform well on real images. This is because graphics generation pipelines require numerous design decisions such as the selection of 3D shapes and the placement of the camera. In this dissertation, we propose automatic generation pipelines of synthetic data that aim to improve the task performance of a trained network. We explore both supervised and unsupervised directions for automatic optimization of 3D decisions. For supervised learning, we demonstrate how to optimize 3D parameters such that a trained network can generalize well to real images. We first show that we can construct a pure synthetic 3D shape to achieve state-of-the-art performance on a shape-from-shading benchmark. We further parameterize the decisions as a vector and propose a hybrid gradient approach to efficiently optimize the vector towards usefulness. Our hybrid gradient is able to outperform classic black-box approaches on a wide selection of 3D perception tasks. For unsupervised learning, we propose a novelty metric for 3D parameter evolution based on deep autoregressive models. We show that without any extrinsic motivation, the novelty computed from autoregressive models alone is helpful. Our novelty metric can consistently encourage a random synthetic generator to produce more useful training data for downstream 3D perception tasks.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163240/1/ydawei_1.pd