SIGGRAPH 2016 Papers

We present SketchiMo, a novel approach for the expressive editing of articulated character motion. SketchiMo solves for the motion given a set of projective constraints that relate the sketch inputs to the unknown 3 D poses. We introduce the concept of sketch space, a contextual geometric representation of sketch targets---motion properties that are editable via sketch input---that enhances, right on the viewport, different aspects of the motion. The combination of the proposed sketch targets and space allows for seamless editing of a wide range of properties, from simple joint trajectories to local parent-child spatiotemporal relationships and more abstract properties such as coordinated motions. This is made possible by interpreting the user's input through a new sketch-based optimization engine in a uniform way. In addition, our view-dependent sketch space also serves the purpose of disambiguating the user inputs by visualizing their range of effect and transparently defining the necessary constraints to set the temporal boundaries for the optimization.

With existing programming tools, writing high-performance simulation code is labor intensive and requires sacrificing readability and portability. The alternative is to prototype simulations in a high-level language like Matlab, thereby sacrificing performance. The Matlab programming model naturally describes the behavior of an entire physical system using the language of linear algebra. However, simulations also manipulate individual geometric elements, which are best represented using linked data structures like meshes. Translating between the linked data structures and linear algebra comes at significant cost, both to the programmer and to the machine. High-performance implementations avoid the cost by rephrasing the computation in terms of linked or index data structures, leaving the code complicated and monolithic, often increasing its size by an order of magnitude. In this article, we present Simit, a new language for physical simulations that lets the programmer view the system both as a linked data structure in the form of a hypergraph and as a set of global vectors, matrices, and tensors depending on what is convenient at any given time. Simit provides a novel assembly construct that makes it conceptually easy and computationally efficient to move between the two abstractions. Using the information provided by the assembly construct, the compiler generates efficient in-place computation on the graph. We demonstrate that Simit is easy to use: a Simit program is typically shorter than a Matlab program; that it is high performance: a Simit program running sequentially on a CPU performs comparably to hand-optimized simulations; and that it is portable: Simit programs can be compiled for GPUs with no change to the program, delivering 4 to 20× speedups over our optimized CPU code.

Shadow theatre is a genre of performance art in which the actors are only visible as shadows projected on the screen. The goal of this study is to generate animated characters, the shadows of which match a sequence of target silhouettes. This poses several challenges. The motion of multiple characters are carefully coordinated to form a target silhouette on the screen, and each character's pose should be stable, balanced, and plausible. The resulting character animation should be smooth and coherent spatially and temporally. We formulate the problem as nonlinear constrained optimization with objectives, which were designed to generate plausible human motions. Our optimization algorithm was primarily inspired by the heuristic strategies of professional shadow theatre actors. Their know-how was studied and then incorporated into our optimization formulation. We demonstrate the effectiveness of our approach with a variety of target silhouettes and 3D fabrication of the results.

We present a real-time, interactive system for the geometric reconstruction, object-class segmentation and learning of 3D scenes [Valentin et al. 2015]. Using our system, a user can walk into a room wearing a depth camera and a virtual reality headset, and both densely reconstruct the 3D scene [Newcombe et al. 2011; Nießner et al. 2013; Prisacariu et al. 2014]) and interactively segment the environment into object classes such as 'chair', 'floor' and 'table'. The user interacts physically with the real-world scene, touching objects and using voice commands to assign them appropriate labels. These user-generated labels are leveraged by an online random forest-based machine learning algorithm, which is used to predict labels for previously unseen parts of the scene. The predicted labels, together with those provided directly by the user, are incorporated into a dense 3D conditional random field model, over which we perform mean-field inference to filter out label inconsistencies. The entire pipeline runs in real time, and the user stays 'in the loop' throughout the process, receiving immediate feedback about the progress of the labelling and interacting with the scene as necessary to refine the predicted segmentation.

We describe a new approach for the purely Eulerian simulation of incompressible fluids. In it, the fluid state is represented by a C 2 -valued wave function evolving under the Schrödinger equation subject to incompressibility constraints. The underlying dynamical system is Hamiltonian and governed by the kinetic energy of the fluid together with an energy of Landau-Lifshitz type. The latter ensures that dynamics due to thin vortical structures, all important for visual simulation, are faithfully reproduced. This enables robust simulation of intricate phenomena such as vortical wakes and interacting vortex filaments, even on modestly sized grids. Our implementation uses a simple splitting method for time integration, employing the FFT for Schrödinger evolution as well as constraint projection. Using a standard penalty method we also allow arbitrary obstacles. The resulting algorithm is simple, unconditionally stable, and efficient. In particular it does not require any Lagrangian techniques for advection or to counteract the loss of vorticity. We demonstrate its use in a variety of scenarios, compare it with experiments, and evaluate it against benchmark tests. A full implementation is included in the ancillary materials.

Rotoscoping (cutting out different characters/objects/layers in raw video footage) is a ubiquitous task in modern post-production and represents a significant investment in person-hours. In this work, we study the particular task of professional rotoscoping for high-end, live action movies and propose a new framework that works with roto-artists to accelerate the workflow and improve their productivity. Working with the existing keyframing paradigm, our first contribution is the development of a shape model that is updated as artists add successive keyframes. This model is used to improve the output of traditional interpolation and tracking techniques, reducing the number of keyframes that need to be specified by the artist. Our second contribution is to use the same shape model to provide a new interactive tool that allows an artist to reduce the time spent editing each keyframe. The more keyframes that are edited, the better the interactive tool becomes, accelerating the process and making the artist more efficient without compromising their control. Finally, we also provide a new, professionally rotoscoped dataset that enables truly representative, real-world evaluation of rotoscoping methods. We used this dataset to perform a number of experiments, including an expert study with professional roto-artists, to show, quantitatively, the advantages of our approach.

Image processing algorithms implemented using custom hardware or FPGAs of can be orders-of-magnitude more energy efficient and performant than software. Unfortunately, converting an algorithm by hand to a hardware description language suitable for compilation on these platforms is frequently too time consuming to be practical. Recent work on hardware synthesis of high-level image processing languages demonstrated that a single-rate pipeline of stencil kernels can be synthesized into hardware with provably minimal buffering. Unfortunately, few advanced image processing or vision algorithms fit into this highly-restricted programming model. In this paper, we present Rigel, which takes pipelines specified in our new multi-rate architecture and lowers them to FPGA implementations. Our flexible multi-rate architecture supports pyramid image processing, sparse computations, and space-time implementation tradeoffs. We demonstrate depth from stereo, Lucas-Kanade, the SIFT descriptor, and a Gaussian pyramid running on two FPGA boards. Our system can synthesize hardware for FPGAs with up to 436 Megapixels/second throughput, and up to 297x faster runtime than a tablet-class ARM CPU.

We propose a novel approach to digital character animation, combining the benefits of modular and tangible input devices and sophisticated rig animation algorithms. With a symbiotic software and hardware approach, we overcome limitations inherent to all previous tangible devices. It allows users to directly control complex rigs with 5-10 physical controls only. These compact input device configurations - optimized for a specific rig and a set of sample poses - are automatically generated by our algorithm. This avoids oversimplification of the pose space and excessively bulky devices.

This paper presents Rich360, a novel system for creating and viewing a 360° panoramic video obtained from multiple cameras placed on a structured rig. Rich360 provides an as-rich-as-possible 360° viewing experience by effectively resolving two issues that occur in the existing pipeline. First, a deformable spherical projection surface is utilized to minimize the parallax from multiple cameras. The surface is deformed spatio-temporally according to the depth constraints estimated from the overlapping video regions. This enables fast and efficient parallax-free stitching independent of the number of views. Next, a non-uniform spherical ray sampling is performed. The density of the sampling varies depending on the importance of the image region. Finally, for interactive viewing, the non-uniformly sampled video is mapped onto a uniform viewing sphere using a UV map. This approach can preserve the richness of the input videos when the resolution of the final 360° panoramic video is smaller than the overall resolution of the input videos, which is the case for most 360° panoramic videos. We show various results from Rich360 to demonstrate the richness of the output video and the advancement in the stitching results.

We extend parametric texture synthesis to capture rich, spatially varying parametric reflectance models from a single image. Our input is a single head-lit flash image of a mostly flat, mostly stationary (textured) surface, and the output is a tile of SVBRDF parameters that reproduce the appearance of the material. No user intervention is required. Our key insight is to make use of a recent, powerful texture descriptor based on deep convolutional neural network statistics for "softly" comparing the model prediction and the examplars without requiring an explicit point-to-point correspondence between them. This is in contrast to traditional reflectance capture that requires pointwise constraints between inputs and outputs under varying viewing and lighting conditions. Seen through this lens, our method is an indirect algorithm for fitting photorealistic SVBRDFs. The problem is severely ill-posed and non-convex. To guide the optimizer towards desirable solutions, we introduce a soft Fourier-domain prior for encouraging spatial stationarity of the reflectance parameters and their correlations, and a complementary preconditioning technique that enables efficient exploration of such solutions by L-BFGS, a standard non-linear numerical optimizer.

We present a novel approach for the automatic creation of a personalized high-quality 3D face rig of an actor from just monocular video data (e.g., vintage movies). Our rig is based on three distinct layers that allow us to model the actor’s facial shape as well as capture his person-specific expression characteristics at high fidelity, ranging from coarse-scale geometry to fine-scale static and transient detail on the scale of folds and wrinkles. At the heart of our approach is a parametric shape prior that encodes the plausible subspace of facial identity and expression variations. Based on this prior, a coarse-scale reconstruction is obtained by means of a novel variational fitting approach. We represent person-specific idiosyncrasies, which cannot be represented in the restricted shape and expression space, by learning a set of medium-scale corrective shapes. Fine-scale skin detail, such as wrinkles, are captured from video via shading-based refinement, and a generative detail formation model is learned. Both the medium- and fine-scale detail layers are coupled with the parametric prior by means of a novel sparse linear regression formulation. Once reconstructed, all layers of the face rig can be conveniently controlled by a low number of blendshape expression parameters, as widely used by animation artists. We show captured face rigs and their motions for several actors filmed in different monocular video formats, including legacy footage from YouTube, and demonstrate how they can be used for 3D animation and 2D video editing. Finally, we evaluate our approach qualitatively and quantitatively and compare to related state-of-the-art methods.

We examine two widely used classes of methods for static analysis of masonry buildings: linear elasticity analysis using finite elements and equilibrium methods. It is often claimed in the literature that finite element analysis is less accurate than equilibrium analysis when it comes to masonry analysis; we examine and qualify this claimed inaccuracy, provide a systematic explanation for the discrepancy observed between their results, and present a unified formulation of the two approaches to stability analysis. We prove that both approaches can be viewed as equivalent, dual methods for getting the same answer to the same problem. We validate our observations with simulations and physical tilt experiments of structures.

Skinning algorithms that work across a broad range of character designs and poses are crucial to creating compelling animations. Currently, linear blend skinning (LBS) and dual quaternion skinning (DQS) are the most widely used, especially for real-time applications. Both techniques are efficient to compute and are effective for many purposes. However, they also have many well-known artifacts, such as collapsing elbows, candy wrapper twists, and bulging around the joints. Due to the popularity of LBS and DQS, it would be of great benefit to reduce these artifacts without changing the animation pipeline or increasing the computational cost significantly. In this paper, we introduce a new direct skinning method that addresses this problem. Our key idea is to pre-compute the optimized center of rotation for each vertex from the rest pose and skinning weights. At runtime, these centers of rotation are used to interpolate the rigid transformation for each vertex. Compared to other direct skinning methods, our method significantly reduces the artifacts of LBS and DQS while maintaining real-time performance and backwards compatibility with the animation pipeline.

In this paper, we show that applying a linear transformation---represented by a 3 x 3 matrix---to the direction vectors of a spherical distribution yields another spherical distribution, for which we derive a closed-form expression. With this idea, we can use any spherical distribution as a base shape to create a new family of spherical distributions with parametric roughness, elliptic anisotropy and skewness. If the original distribution has an analytic expression, normalization, integration over spherical polygons, and importance sampling, then these properties are inherited by the linearly transformed distributions. By choosing a clamped cosine for the original distribution we obtain a family of distributions, which we call Linearly Transformed Cosines (LTCs), that provide a good approximation to physically based BRDFs and that can be analytically integrated over arbitrary spherical polygons. We show how to use these properties in a realtime polygonal-light shading application. Our technique is robust, fast, accurate and simple to implement.

We present a novel image-based representation for dynamic 3D avatars, which allows effective handling of various hairstyles and headwear, and can generate expressive facial animations with fine-scale details in real-time. We develop algorithms for creating an image-based avatar from a set of sparsely captured images of a user, using an off-the-shelf web camera at home. An optimization method is proposed to construct a topologically consistent morphable model that approximates the dynamic hair geometry in the captured images. We also design a real-time algorithm for synthesizing novel views of an image-based avatar, so that the avatar follows the facial motions of an arbitrary actor. Compelling results from our pipeline are demonstrated on a variety of cases.