Despite the recent advances in automatic methods for computing skinning weights, manual intervention is still indispensable to produce high quality character deformation. However, current modeling software does not provide efficient tools for the manual definition of skinning weights. A widely used paint-based interface gives a users high degrees of freedom, but at the expense of significant efforts and time. This paper presents a novel interface for editing skinning weights based on splines, which represent the isolines of skinning weights on a mesh. When a user drags a small number of spline anchor points, our method updates the shape of the isolines and smoothly interpolates or propagates the weights while respecting the given iso-value on the spline. We introduce several techniques to enable the interface to run in real-time, and propose a particular combination of functions that generates appropriate skinning weight distribution over the surface given splines. Users can create skinning weight from the scratch by using our method. In addition, we present the spline and the gradient fitting methods that closely approximate an initial weight made by an automatic method or a paint interface, so that a user can modify the given weight with our spline interface.We show the effectiveness of our spline-based interface through a number of test cases.
We present the first computational approach that can transform 3D meshes, created by traditional modeling programs, directly into instructions for a computer-controlled knitting machine.Knitting machines are able to robustly and repeatably form knitted 3D surfaces from yarn, but have many constraints on what they can fabricate. Given user-defined starting and ending points on an input mesh, our system incrementally builds a helix-free, quad-dominant mesh with uniform edge lengths, runs a tracing procedure over this mesh to generate a knitting path, and schedules the knitting instructions for this path in a way that is compatible with machine constraints. We demonstrate our approach on a wide range of 3D meshes.
Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, we propose a method that is able to compute a rich and realistic 3D model of a human body and its outfits from a single photograph with little human interaction. Our algorithm is not only able to capture the global shape and geometry of the clothing, it can also extract small but important details of cloth, such as occluded wrinkles and folds. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or sampled 3D geometry), our approach achieves detailed garment recovery from a single-view image by using statistical, geometric, and physical priors and a combination of parameter estimation, semantic parsing, shape recovery, and physics-based cloth simulation. We demonstrate the effectiveness of our algorithm by re-purposing the reconstructed garments for virtual try-on and garment transfer applications, as well as cloth animation for digital characters.
We present an efficient spacetime optimization method to automatically generate animations for a general volumetric, elastically deformable body. Our approach can model the interactions between the body and the environment and automatically generate active animations. We model the frictional contact forces using contact invariant optimization and the fluid drag forces using a simplified model. To handle complex objects, we use a reduced deformable model and present a novel hybrid optimizer to search for the local minima efficiently. This allows us to use long-horizon motion planning to automatically generate animations such as walking, jumping, swimming, and rolling. We evaluate the approach on different shapes and animations, including deformable body navigation and combining with an open-loop controller for realtime forward simulation.
We present an integrated approach for reconstructing high-fidelity 3D models using consumer RGB-D cameras. RGB-D registration and reconstruction algorithms are prone to errors from scanning noise, making it hard to perform 3D reconstruction accurately. The key idea of our method is to assign a probabilistic uncertainty model to each depth measurement, which then guides the scan alignment and depth fusion. This allows us to effectively handle inherent noise and distortion in depth maps while keeping the overall scan registration procedure under the iterative closest point (ICP) frame-work for simplicity and efficiency. We further introduce a local-to-global, submap-based, and uncertainty-aware global pose optimization scheme to improve scalability and guarantee global model consistency. Finally, we have implemented the proposed algorithm on the GPU, achieving real-time 3D scanning frame rates and updating the reconstructed model on-the-fly. Experimental results on simulated and real-world data demonstrate that the proposed method outperforms state-of-the-art systems in terms of the accuracy of both recovered camera trajectories and reconstructed models.
High dynamic range (HDR) tone mapping is formulated as an optimization problem of maximizing perceivable spatial details given the limited dynamic range of display devices. This objective can be attained, as supported by our results, by a novel image display methodology called locally adaptive rank-constrained optimal tone mapping (LARCOTM). The scientific basis for LARCOTM is that the maximum discrimination power of human vision system can only be achieved in a relatively small locality of an image. LARCOTM is fundamentally different from existing HDR tone mapping techniques in that the former can preserve pixel value order statistics within localities in which human foveal vision retains maximum sensitivity, while the latter cannot. As a result, images enhanced by LARCOTM are free of artifacts such as halos and double edges that plague other HDR methods.
Simulating (elastically) deformable models that can collide with each other and with the environment remains a challenging task. The resulting contact problems can be elegantly approached using Lagrange multipliers to represent the unknown magnitude of the response forces. Typical methods construct and solve a Linear Complementarity Problem (LCP) to obtain the response forces. This requires the inverse of the generalized mass matrix, which is in general hard to obtain for deformable-body problems. In this paper we tackle such contact problems by directly solving the Mixed Linear Complementarity Problem (MLCP) and omitting the construction of an LCP matrix. Since the MLCP is equivalent to a convex quadratic program subject to inequality constraints, we propose to use a Conjugate Residual (CR) solver as the backbone of our collision-response system. We also propose a simple yet efficient preconditioner that ensures faster convergence. Finally, our approach is faster than existing methods (at the same accuracy), and it allows accurate treatment of friction.
Computation of contact points is a critical sub-component for physics based animation. The success and correctness of simulation results are very sensitive to the quality of the contact points. Hence, quality plays a critical role when comparing methods and is highly relevant for simulating objects with sharp edges. The importance of contact point quality is largely overlooked and lacks rigor and as such may become a bottleneck in moving the research field forward. We establish a taxonomy of contact point generation methods, and lay down an analysis of what normal contact quality implies. The analysis enables us to establish a novel methodology for assessing and studying quality. The core is based on a test-suite of one complex pillar and 8 simple pitfall examples. We apply our methodology to 8 local contact point generation methods and conclude from these studies that the selected local methods are inferior in terms of providing 100% correct information in all cases. The benefit of the proposed methodology is a foundation for others to select a local method bet- ter suitable, meaning lesser evil, for their specific application context. In the longer perspective the presented work strongly suggest future research to focus on semi-local methods.
Walt Disney Animation Studios has transitioned to path-traced global illumination as part of a progression of brute-force physically based rendering in the name of artist efficiency. To achieve this without compromising our geometric or shading complexity, we built our Hyperion renderer based on a novel architecture that extracts traversal and shading coherence from large, sorted ray batches. In this paper we describe our architecture and discuss our design decisions. We also explain how we are able to provide artistic control in a physically based renderer, and we demonstrate through case studies how we have benefited from having a proprietary renderer that can evolve with production needs.
Many strategies exist for optimizing non-linear distortion energies in geometry and physics applications, but devising an approach that achieves the convergence promised by Newton-type methods remains challenging. In order to guarantee the positive semi-definiteness required by these methods, a numerical eigendecomposition or approximate regularization is usually needed. In this paper, we present analytic expressions for the eigensystems at each quadrature point of a wide range of isotropic distortion energies. These systems can then be used to project energy Hessians to positive semi-definiteness analytically. Unlike previous attempts, our formulation provides compact expressions that are valid both in 2D and 3D, and does not introduce spurious degeneracies. At its core, our approach utilizes the invariants of the stretch tensor that arises from the polar decomposition of the deformation gradient. We provide closed-form expressions for the eigensystems for all these invariants, and use them to systematically derive the eigensystems of any isotropic energy. Our results are suitable for geometry optimization over flat surfaces or volumes, and agnostic to both the choice of discretization and basis function. To demonstrate the efficiency of our approach, we include comparisons against existing methods on common graphics tasks such as surface parameterization and volume deformation.
This article presents an iterative backward-warping technique and its applications. It predictively synthesizes depth buffers for novel views. Our solution is based on the fixed-point iteration that converges quickly in practice. Unlike this previous technique, our solution is a purely backward warping without using bidirectional sources. To efficiently seed the iterative process, we also propose a tight bounding method for motion vectors. Non-convergent depth holes are inpainted via deep depth buffers. Our solution works well with arbitrarily distributed motion vectors under moderate motions. Many scenarios can benefit from our depth warping. As an application, we propose a highly scalable image-based occlusion-culling technique, achieving a significant speedup compared to the state of the art. We also demonstrate the benefit of our solution in multi-view soft-shadow generation.
A large number of imaging and computer graphics applications require localized information on the visibility of image distortions. Existing image quality metrics are not suitable for this task as they provide a single quality value per image. Existing visibility metrics produce visual difference maps, and are specifically designed for detecting just noticeable distortions but their predictions are often inaccurate. In this work, we argue that the key reason for this problem is the lack of large image collections with a good coverage of possible distortions that occur in different applications. To address the problem, we collect an extensive dataset of reference and distorted image pairs together with user markings indicating whether distortions are visible or not. We propose a statistical model that is designed for the meaningful interpretation of such data, which is affected by visual search and imprecision of manual marking. We use our dataset for training existing metrics and we demonstrate that their performance significantly improves. We show that our dataset with the proposed statistical model can be used to train a new CNN-based metric, which outperforms the existing solutions. We demonstrate the utility of such a metric in visually lossless JPEG compression, super-resolution and watermarking.
Quadrotor drones equipped with high quality cameras have rapidely raised as novel, cheap and stable devices for filmmakers. While professional drone pilots can create aesthetically pleasing videos in short time, the smooth and cinematographic control of a camera drone remains challenging for most users, despite recent tools that either automate part of the process or enable the manual design of waypoints to create drone trajectories. This paper proposes to move a step further towards more accessible cinematographic drones by designing techniques to automatically or interactively plan quadrotor drone motions in 3D dynamic environments that satisfy both cinematographic and physical quadrotor constraints. We first propose the design of a Drone Toric Space as a dedicated camera parameter space with embedded constraints and derive some intuitive on-screen viewpoint manipulators. Second, we propose a specific path planning technique which ensures both that cinematographic properties can be enforced along the path, and that the path is physically feasible by a quadrotor drone. At last, we build on the Drone Toric Space and the specific path planning technique to coordinate the motion of multiple drones around dynamic targets. A number of results then demonstrate the interactive and automated capacities of our approaches on a number of use-cases.
Pixar's RenderMan renderer is used to render all of Pixar's films, and by many film studios to render visual effects for live-action movies. RenderMan started as a scanline renderer based on the Reyes algorithm, and was extended over the years with ray tracing and several global illumination algorithms. This paper describes the modern version of RenderMan, a new architecture for an extensible and programmable path tracer with many features that are essential to handle the fiercely complex scenes in movie production. Users can write their own materials using a bxdf interface, and their own light transport algorithms using an integrator interface -- or they can use the materials and light transport algorithms provided with RenderMan. Complex geometry and textures are handled with efficient multi-resolution representations, with resolution chosen using path differentials. We trace rays and shade ray hit points in medium-sized groups, which provides the benefits of SIMD execution without excessive memory overhead or data streaming. The path-tracing architecture handles surface, subsurface, and volume scattering. We show examples of the use of path tracing, bidirectional path tracing, VCM, and UPBP light transport algorithms. We also describe our progressive rendering for interactive use and our adaptation of denoising techniques.
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Subtractive manufacturing technologies, such as 3-axis milling, add a useful tool to the digital manufacturing arsenal. However, each milling pass using such machines can only carve a single height-field, defined with respect to the machine tray. We enable fabrication of general shapes using 3-axis milling, providing a robust algorithm to decompose any shape into a few height-field blocks. Such blocks can be manufactured with a single milling pass and then assembled to form the target geometry. Computing such decompositions requires solving a complex discrete optimization problem, variants of which are known to be NP-hard. We propose a two-step process, based on the observation that if the height-field directions are constrained to the major axes we can guarantee a valid decomposition starting from a suitable surface segmentation. Our method first produces a compact set of large, possibly overlapping, height-field blocks that jointly cover the model surface. We then compute an overlap-free decomposition via a combination of cycle elimination and topological sorting on a graph. The algorithm produces a compact set of height-field blocks that jointly describe the input model and satisfy all manufacturing constraints. We demonstrate our method on a range of inputs, and showcase several real life models manufactured using our technique.
The Manuka rendering architecture has been designed to enable visually rich computer generated imagery for visual effects in movie production. This means supporting extremely complex geometry, texturing and shading. In the current generation of renderers, it is essential to support very accurate global illumination as a means to naturally tie together different assets in a picture. This is achieved with Monte Carlo path tracing, using a paradigm often called shade on hit, in which the renderer alternates tracing rays with running shaders on the various ray hits. The shaders take the role of generating the inputs of the local material structure which is then used by path sampling logic to evaluate contributions and to inform what further rays to cast through the scene. We propose a shade before hit paradigm instead and minimise I/O strain on the system, leveraging locality of reference by running pattern generation shaders before we execute light transport simulation by path sampling. We describe a full architecture built around this approach, featuring spectral light transport and a flexible implementation of multiple importance sampling, resulting in a system able to support a comparable amount of extensibility to what made the reyes rendering architecture successful over many decades.
Arnold is a physically-based renderer for feature-length animation and visual effects. Arnold has been created to take on the challenge of making the simple and elegant approach of brute-force Monte Carlo path tracing practical for production rendering. Achieving this requires building a robust piece of ray tracing software that can ingest large amounts of geometry with detailed shading and lighting and produce images with high fidelity, while scaling well with the available memory and processing power. Arnold's guiding principles are to expose as few controls as possible, provide rapid feedback to artists, and adapt to various production workflows. In this paper we describe its architecture with a focus on the design and implementation choices made during its evolutionary development to meet the aforementioned requirements and goals. Arnold's workhorse is a unidirectional path tracer that avoids the use of hard to manage and artifact-prone caching and sits on top of a ray tracing engine optimized to shoot and shade billions of spatially incoherent rays throughout a scene. A comprehensive API provides the means to configure and extend the system's functionality, to describe a scene, render it, and write the results to dis
The computational cost for creating realistic fluid animations by numerical simulation is generally expensive. In digital production environments, existing precomputed fluid animations are often reused for different scenes in order to reduce the cost of creating scenes containing fluids. However, applying the same animation to different scenes often produces unacceptable results, so the animation needs to be edited. In order to help animators with the editing process, we develop a novel method for synthesizing the desired flow fields by combining existing flow field data. Our system allows the user to place flow fields at desired positions, and combine them by interpolation at the boundaries between these flow fields, to synthesize a new flow field. The interpolation of the flow fields is realized by minimizing an energy function, which is designed to satisfy the incompressible Navier-Stokes equations. Our method focuses on smoke simulations defined on a uniform grid. We demonstrate the potential of our method by showing a set of examples, including a large-scale sandstorm created from a few flow fields simulated in a small-scale space.
In this paper we describe a complete pipeline for the capture and display of real-world Virtual Reality video content, based on the concept of omnistereoscopic panoramas. We address important practical and theoretical issues that have remained undiscussed in previous works. On the capture side we show how high quality omnistereo video can be generated from a sparse set of cameras (16 in our prototype array) instead of the hundreds of input views previously required. Despite the sparse number of input views, our approach allows for high quality, real-time virtual head motion, thereby providing an important additional cue for immersive depth perception compared to static stereoscopic video. We also provide an in depth analysis of the required camera array geometry in order to meet specific stereoscopic output constraints, which is fundamental for achieving a plausible and fully controlled VR viewing experience. Finally, we describe additional insights on how to integrate omnistereo video panoramas with rendered CG content. We provide qualitative comparisons to alternative solutions, including depth-based view synthesis and the Facebook Surround 360 system. In summary, this paper provides a first complete guide and analysis for reimplementing a system for capturing and displaying real-world VR, which we demonstrate on several real-world examples captured with our prototype.
In this paper, we introduce a surface reconstruction method that can perform gracefully with non-uniformly-distributed, noisy, and even sparse data. We reconstruct the surface by estimating an implicit function and then obtain a triangle mesh by extracting an iso-surface from it. Our implicit function takes advantage of both the indicator function and the signed distance function. It is dominated by the indicator function at the regions away from the surface and approximates the signed distance function near the surface. On one hand, it is well defined over the entire space so that the extracted iso-surface must lie near the underlying true surface and is free of spurious sheets. On the other hand, thanks to the nice properties of the signed distance function, a smooth iso-surface can be extracted using the approach of marching cubes with simple linear interpolations. More importantly, our implicit function can be estimated directly from an explicit integral formula without solving any linear system. This direct approach leads to a simple, accurate and robust reconstruction method, which can be paralleled with little overhead. We apply our method to both synthetic and real-world scanned data and demonstrate the accuracy, robustness and efficiency of our method.
Everyone uses the sense of touch to explore the world, and roughness is one of the most important qualities in tactile perception. Roughness is a major identifier for judgments of material composition, comfort and friction, and it is tied closely to manual dexterity. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with surface geometry that confers haptic properties. In this work, we address the problem of mapping object geometry to tactile roughness. We fabricate a set of carefully designed stimuli and use them in experiments with human subjects to build a perceptual space for roughness. We then match this space to a quantitative model obtained from strain fields derived from elasticity simulations of the human skin contacting the texture geometry, drawing from past research in neuroscience and psychophysics. We demonstrate how this model can be applied to predict and alter surface roughness, and we show several applications in the context of fabrication.