DeepGarment : 3D Garment Shape Estimation from a Single Image

3D garment capture is an important component for various applications such as free‐view point video, virtual avatars, online shopping, and virtual cloth fitting. Due to the complexity of the deformations, capturing 3D garment shapes requires controlled and specialized setups. A viable alternative is image‐based garment capture. Capturing 3D garment shapes from a single image, however, is a challenging problem and the current solutions come with assumptions on the lighting, camera calibration, complexity of human or mannequin poses considered, and more importantly a stable physical state for the garment and the underlying human body. In addition, most of the works require manual interaction and exhibit high run‐times. We propose a new technique that overcomes these limitations, making garment shape estimation from an image a practical approach for dynamic garment capture. Starting from synthetic garment shape data generated through physically based simulations from various human bodies in complex poses obtained through Mocap sequences, and rendered under varying camera positions and lighting conditions, our novel method learns a mapping from rendered garment images to the underlying 3D garment model. This is achieved by training Convolutional Neural Networks (CNN‐s) to estimate 3D vertex displacements from a template mesh with a specialized loss function. We illustrate that this technique is able to recover the global shape of dynamic 3D garments from a single image under varying factors such as challenging human poses, self occlusions, various camera poses and lighting conditions, at interactive rates. Improvement is shown if more than one view is integrated. Additionally, we show applications of our method to videos.     

Intrinsic Images by Clustering

Decomposing an input image into its intrinsic shading and reflectance components is a long‐standing ill‐posed problem. We present a novel algorithm that requires no user strokes and works on a single image. Based on simple assumptions about its reflectance and luminance, we first find clusters of similar reflectance in the image, and build a linear system describing the connections and relations between them. Our assumptions are less restrictive than widely‐adopted Retinex‐based approaches, and can be further relaxed in conflicting situations. The resulting system is robust even in the presence of areas where our assumptions do not hold. We show a wide variety of results, including natural images, objects from the MIT dataset and texture images, along with several applications, proving the versatility of our method.     

Pixel History Linear Models for Real‐Time Temporal Filtering

We propose a new real‐time temporal filtering and antialiasing (AA) method for rasterization graphics pipelines. Our method is based on Pixel History Linear Models (PHLM), a new concept for modeling the history of pixel shading values over time using linear models. Based on PHLM, our method can predict per‐pixel variations of the shading function between consecutive frames. This combines temporal reprojection with per‐pixel shading predictions in order to provide temporally coherent shading, even in the presence of very noisy input images. Our method can address both spatial and temporal aliasing problems under a unique filtering framework that minimizes filtering error through a recursive least squares algorithm. We demonstrate our method working with a commercial deferred shading engine for rasterization and with our own OpenGL deferred shading renderer. We have implemented our method in GPU and it has shown significant reduction of temporal flicker in very challenging scenarios including foliage rendering, complex non‐linear camera motions, dynamic lighting, reflections, shadows and fine geometric details. Our approach, based on PHLM, avoids the creation of visible ghosting artifacts and it reduces the filtering overblur characteristic of temporal deflickering methods. At the same time, the results are comparable to state‐of‐the‐art real‐time filters in terms of temporal coherence.     

Modeling Complex Unfoliaged Trees from a Sparse Set of Images

We present a novel image‐based technique for modeling complex unfoliaged trees. Existing tree modeling tools either require capturing a large number of views for dense 3D reconstruction or rely on user inputs and botanic rules to synthesize natural‐looking tree geometry. In this paper, we focus on faithfully recovering real instead of realistically‐looking tree geometry from a sparse set of images. Our solution directly integrates 2D/3D tree topology as shape priors into the modeling process. For each input view, we first estimate a 2D skeleton graph from its matte image and then find a 2D skeleton tree from the graph by imposing tree topology. We develop a simple but effective technique for computing the optimal 3D skeleton tree most consistent with the 2D skeletons. For each edge in the 3D skeleton tree, we further apply volumetric reconstruction to recover its corresponding curved branch. Finally, we use piecewise cylinders to approximate each branch from the volumetric results. We demonstrate our framework on a variety of trees to illustrate the robustness and usefulness of our technique.     

Image-based Material Weathering

The appearance manifold [WTL*06] is an efficient approach for modeling and editing time‐variant appearance of materials from the BRDF data captured at single time instance. However, this method is difficult to apply in images in which weathering and shading variations are combined. In this paper, we present a technique for modeling and editing the weathering effects of an object in a single image with appearance manifolds. In our approach, we formulate the input image as the product of reflectance and illuminance. An iterative method is then developed to construct the appearance manifold in color space (i.e., Lab space) for modeling the reflectance variations caused by weathering. Based on the appearance manifold, we propose a statistical method to robustly decompose reflectance and illuminance for each pixel. For editing, we introduce a “pixel‐walking” scheme to modify the pixel reflectance according to its position on the manifold, by which the detailed reflectance variations are well preserved. We illustrate our technique in various applications, including weathering transfer between two images that is first enabled by our technique. Results show that our technique can produce much better results than existing methods, especially for objects with complex geometry and shading effects.     

Multi‐View Stereo on Consistent Face Topology

We present a multi‐view stereo reconstruction technique that directly produces a complete high‐fidelity head model with consistent facial mesh topology. While existing techniques decouple shape estimation and facial tracking, our framework jointly optimizes for stereo constraints and consistent mesh parameterization. Our method is therefore free from drift and fully parallelizable for dynamic facial performance capture. We produce highly detailed facial geometries with artist‐quality UV parameterization, including secondary elements such as eyeballs, mouth pockets, nostrils, and the back of the head. Our approach consists of deforming a common template model to match multi‐view input images of the subject, while satisfying cross‐view, cross‐subject, and cross‐pose consistencies using a combination of 2D landmark detection, optical flow, and surface and volumetric Laplacian regularization. Since the flow is never computed between frames, our method is trivially parallelized by processing each frame independently. Accurate rigid head pose is extracted using a PCA‐based dimension reduction and denoising scheme. We demonstrate high‐fidelity performance capture results with challenging head motion and complex facial expressions around eye and mouth regions. While the quality of our results is on par with the current state‐of‐the‐art, our approach can be fully parallelized, does not suffer from drift, and produces face models with production‐quality mesh topologies.     

Dynamic Multi‐View Exploration of Shape Spaces

Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model models the distribution in a high dimensional shape space, where each shape is represented by a single point. We present a design study on the intuitive exploration and visualization of shape spaces and shape models. Our approach focuses on the dual‐space nature of these spaces. The high‐dimensional shape space represents the population, whereas object space represents the shape of the 3D object associated with a point in shape space. A 3D object view provides local details for a single shape. The high dimensional points in shape space are visualized using a 2D scatter plot projection, the axes of which can be manipulated interactively. This results in a dynamic scatter plot, with the further extension that each point is visualized as a small version of the object shape that it represents. We further enhance the population‐object duality with a new type of view aimed at shape comparison. This new “shape evolution view” visualizes shape variability along a single trajectory in shape space, and serves as a link between the two spaces described above. Our three‐view exploration concept strongly emphasizes linked interaction between all spaces. Moving the cursor over the scatter plot or evolution views, shapes are dynamically interpolated and shown in the object view. Conversely, camera manipulation in the object view affects the object visualizations in the other views. We present a GPU‐accelerated implementation, and show the effectiveness of the three‐view approach using a number of real‐world cases. In these, we demonstrate how this multi‐view approach can be used to visually explore important aspects of a statistical shape model, including specificity, compactness and reconstruction error.     

Animation‐Aware Quadrangulation

Geometric meshes that model animated characters must be designed while taking into account the deformations that the shape will undergo during animation. We analyze an input sequence of meshes with point‐to‐point correspondence, and we automatically produce a quadrangular mesh that fits well the input animation. We first analyze the local deformation that the surface undergoes at each point, and we initialize a cross field that remains as aligned as possible to the principal directions of deformation throughout the sequence. We then smooth this cross field based on an energy that uses a weighted combination of the initial field and the local amount of stretch. Finally, we compute a field‐aligned quadrangulation with an off‐the‐shelf method. Our technique is fast and very simple to implement, and it significantly improves the quality of the output quad mesh and its suitability for character animation, compared to creating the quad mesh based on a single pose. We present experimental results and comparisons with a state‐of‐the‐art quadrangulation method, on both sequences from 3D scanning and synthetic sequences obtained by a rough animation of a triangulated model.     

Metalights: Improved Interleaved Shading

We present metalights, a novel Virtual Point Light (VPL) encapsulating structure which enhances classic interleaved shading by improving VPL sampling, based on few initial screen space samples to estimate VPL contribution to current view. Our method leads to important noise variance reduction in the final picture by only adding a small fraction of computation. The implementation is straight‐forward and well adapted to both CPU and GPU‐based engines. We also present different image‐space assignment schemes for the VPL subsets to break the regularity of the noise pattern or to adapt it to simple antialiasing.     

Atomistic Visualization of Mesoscopic Whole‐Cell Simulations Using Ray‐Casted Instancing

Molecular visualization is an important tool for analysing the results of biochemical simulations. With modern GPU ray casting approaches, it is only possible to render several million of atoms interactively unless advanced acceleration methods are employed. Whole‐cell simulations consist of at least several billion atoms even for simplified cell models. However, many instances of only a few different proteins occur in the intracellular environment, which can be exploited to fit the data into the graphics memory. For each protein species, one model is stored and rendered once per instance. The proposed method exploits recent algorithmic advances for particle rendering and the repetitive nature of intracellular proteins to visualize dynamic results from mesoscopic simulations of cellular transport processes. We present two out‐of‐core optimizations for the interactive visualization of data sets composed of billions of atoms as well as details on the data preparation and the employed rendering techniques. Furthermore, we apply advanced shading methods to improve the image quality including methods to enhance depth and shape perception besides non‐photorealistic rendering methods. We also show that the method can be used to render scenes that are composed of triangulated instances, not only implicit surfaces.     

An Intuitive Interface for Interactive High Quality Image‐Based Modeling

We present the design of an interactive image‐based modeling tool that enables a user to quickly generate detailed 3D models with texture from a set of calibrated input images. Our main contribution is an intuitive user interface that is entirely based on simple 2D painting operations and does not require any technical expertise by the user or difficult pre‐processing of the input images. One central component of our tool is a GPU‐based multi‐view stereo reconstruction scheme, which is implemented by an incremental algorithm, that runs in the background during user interaction so that the user does not notice any significant response delay.     

Interactive Image‐Guided Modeling of Extruded Shapes

A recent trend in interactive modeling of 3D shapes from a single image is designing minimal interfaces, and accompanying algorithms, for modeling a specific class of objects. Expanding upon the range of shapes that existing minimal interfaces can model, we present an interactive image‐guided tool for modeling shapes made up of extruded parts. An extruded part is represented by extruding a closed planar curve, called base, in the direction orthogonal to the base. To model each extruded part, the user only needs to sketch the projected base shape in the image. The main technical contribution is a novel optimization‐based approach for recovering the 3D normal of the base of an extruded object by exploring both geometric regularity of the sketched curve and image contents. We developed a convenient interface for modeling multi‐part shapes and a method for optimizing the relative placement of the parts. Our tool is validated using synthetic data and tested on real‐world images.     

InK‐Compact: In‐Kernel Stream Compaction and Its Application to Multi‐Kernel Data Visualization on General‐Purpose GPUs

Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g. deferred shading, isosurface extraction and surface voxelization in computer graphics and visualization. We present a novel In‐Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel. We apply our compaction methods to ray‐tracing‐based visualization of volumetric data. We demonstrate that the proposed In‐Kernel compaction outperforms the standard out‐of‐kernel Thrust parallel‐scan method for performing stream compaction in this real‐world application. For the data visualization, we also propose a novel multi‐kernel ray‐tracing pipeline for increased thread coherency and show that it outperforms a conventional single‐kernel approach.     

Matting and Compositing for Fresnel Reflection on Wavy Surfaces

This paper introduces a framework that can extract an alpha matte from a single image with Fresnel reflection, and that can composite other objects with the image such that plausible reflections are included. Our method handles reflections in a plane with small undulations, for example, a water surface with waves or a glossy tabletop. During the matting stage, our method first estimates the transmission color, which is assumed to be uniform, and then calculates a reflection image and alpha matte based on user markups. However, accurate extraction of the matte becomes challenging when a plane has small undulations because these create perturbations in the matte. We therefore propose a filter that can refine the matte effectively. In the compositing stage, the reflection of a composited object is synthesized by ray tracing in real time. We demonstrate the effectiveness of our method through comparisons with ground‐truth data and results using natural images as inputs.     

Single Image Weathering via Exemplar Propagation

This paper presents an efficient approach for generating weathering effects with detailed appearance variations in a single image. Previous approaches merely change chroma or reflectance of weathered objects, which is not sufficient for materials with detailed shading and texture variations, such as growing moss and peeling plaster. Our method propagates such detailed features via seamless patch‐based synthesis driven by weathering degree distribution. Unlike previous methods, the weathering degrees are calculated efficiently using Radial Basis Functions even for materials with wide color variations. We use graph cut‐based optimization to identify the most weathered region as a “weathering exemplar”, from which we sample weathering patches. We demonstrate our method enables us to generate various types of detailed weathering effects interactively.     

Adaptive Image‐Space Sampling for Gaze‐Contingent Real‐time Rendering

With ever‐increasing display resolution for wide field‐of‐view displays—such as head‐mounted displays or 8k projectors—shading has become the major computational cost in rasterization. To reduce computational effort, we propose an algorithm that only shades visible features of the image while cost‐effectively interpolating the remaining features without affecting perceived quality. In contrast to previous approaches we do not only simulate acuity falloff but also introduce a sampling scheme that incorporates multiple aspects of the human visual system: acuity, eye motion, contrast (stemming from geometry, material or lighting properties), and brightness adaptation. Our sampling scheme is incorporated into a deferred shading pipeline to shade the image's perceptually relevant fragments while a pull‐push algorithm interpolates the radiance for the rest of the image. Our approach does not impose any restrictions on the performed shading. We conduct a number of psycho‐visual experiments to validate scene‐ and task‐independence of our approach. The number of fragments that need to be shaded is reduced by 50 % to 80 %. Our algorithm scales favorably with increasing resolution and field‐of‐view, rendering it well‐suited for head‐mounted displays and wide‐field‐of‐view projection.     

Style Transfer Functions for Illustrative Volume Rendering

Illustrative volume visualization frequently employs non-photorealistic rendering techniques to enhance important features or to suppress unwanted details. However, it is difficult to integrate multiple non-photorealistic rendering approaches into a single framework due to great differences in the individual methods and their parameters. In this paper, we present the concept of style transfer functions. Our approach enables flexible data-driven illumination which goes beyond using the transfer function to just assign colors and opacities. An image-based lighting model uses sphere maps to represent non-photorealistic rendering styles. Style transfer functions allow us to combine a multitude of different shading styles in a single rendering. We extend this concept with a technique for curvature-controlled style contours and an illustrative transparency model. Our implementation of the presented methods allows interactive generation of high-quality volumetric illustrations.     

Image‐Based Edge Bundles: Simplified Visualization of Large Graphs

We present a new approach aimed at understanding the structure of connections in edge‐bundling layouts. We combine the advantages of edge bundles with a bundle‐centric simplified visual representation of a graph's structure. For this, we first compute a hierarchical edge clustering of a given graph layout which groups similar edges together. Next, we render clusters at a user‐selected level of detail using a new image‐based technique that combines distance‐based splatting and shape skeletonization. The overall result displays a given graph as a small set of overlapping shaded edge bundles. Luminance, saturation, hue, and shading encode edge density, edge types, and edge similarity. Finally, we add brushing and a new type of semantic lens to help navigation where local structures overlap. We illustrate the proposed method on several real‐world graph datasets.     

On the shape of a set of points and lines in the plane

Detailed geometric models of the real world are in increasing demand. LiDAR data is appropriate to reconstruct urban models. In urban scenes, the individual surfaces can be reconstructed and connected to form the scene geometry. There are various methods for reconstructing the free‐form shape of a point sample on a single surface. However, these methods do not take the context of the surface into account. We present the guided α‐shape: an extension of the well known α‐shape that uses lines (guides) to indicate preferred locations for the boundary of the shape. The guided α‐shape uses (parts of) these lines as boundary where the points suggest that this is appropriate. We prove that the guided α‐shape can be constructed in O((n + m) log (n + m)) time, from an input of n points and m guides. We apply guided α‐shapes to urban reconstruction from LiDAR, where neighboring surfaces can be connected conveniently along their intersection lines into adjacent surfaces of a 3D model. We analyze guided α‐shapes of both synthetic and real data and show they are consistently better than α‐shapes for this application.