SIGGRAPH 2015 论文列表

Programmable coding of light between a source and a sensor has led to several important results in computational illumination, imaging and display. Little is known, however, about how to utilize energy most effectively, especially for applications in live imaging. In this paper, we derive a novel framework to maximize energy efficiency by "homogeneous matrix factorization" that respects the physical constraints of many coding mechanisms (DMDs/LCDs, lasers, etc. ). We demonstrate energy-efficient imaging using two prototypes based on DMD and laser illumination. For our DMD-based prototype, we use fast local optimization to derive codes that yield brighter images with fewer artifacts in many transport probing tasks. Our second prototype uses a novel combination of a low-power laser projector and a rolling shutter camera. We use this prototype to demonstrate never-seen-before capabilities such as (1) capturing live structured-light video of very bright scenes---even a light bulb that has been turned on; (2) capturing epipolar-only and indirect-only live video with optimal energy efficiency; (3) using a low-power projector to reconstruct 3D objects in challenging conditions such as strong indirect light, strong ambient light, and smoke; and (4) recording live video from a projector's---rather than the camera's---point of view.

We present a method for simulating brittle fracture under the assumptions of quasi-static linear elastic fracture mechanics (LEFM). Using the boundary element method (BEM) and Lagrangian crack-fronts, we produce highly detailed fracture surfaces. The computational cost of the BEM is alleviated by using a low-resolution mesh and interpolating the resulting stress intensity factors when propagating the high-resolution crack-front. Our system produces physics-based fracture surfaces with high spatial and temporal resolution, taking spatial variation of material toughness and/or strength into account. It also allows for crack initiation to be handled separately from crack propagation, which is not only more reasonable from a physics perspective, but can also be used to control the simulation. Separating the resolution of the crack-front from the resolution of the computational mesh increases the efficiency and therefore the amount of visual detail on the resulting fracture surfaces. The BEM also allows us to re-use previously computed blocks of the system matrix.

We present the first end-to-end solution to create high-quality free-viewpoint video encoded as a compact data stream. Our system records performances using a dense set of RGB and IR video cameras, generates dynamic textured surfaces, and compresses these to a streamable 3D video format. Four technical advances contribute to high fidelity and robustness: multimodal multi-view stereo fusing RGB, IR, and silhouette information; adaptive meshing guided by automatic detection of perceptually salient areas; mesh tracking to create temporally coherent subsequences; and encoding of tracked textured meshes as an MPEG video stream. Quantitative experiments demonstrate geometric accuracy, texture fidelity, and encoding efficiency. We release several datasets with calibrated inputs and processed results to foster future research.

We introduce gradient-domain rendering for Monte Carlo image synthesis. While previous gradient-domain Metropolis Light Transport sought to distribute more samples in areas of high gradients, we show, in contrast, that estimating image gradients is also possible using standard (non-Metropolis) Monte Carlo algorithms, and furthermore, that even without changing the sample distribution, this often leads to significant error reduction. This broadens the applicability of gradient rendering considerably. To gain insight into the conditions under which gradient-domain sampling is beneficial, we present a frequency analysis that compares Monte Carlo sampling of gradients followed by Poisson reconstruction to traditional Monte Carlo sampling. Finally, we describe Gradient-Domain Path Tracing (G-PT), a relatively simple modification of the standard path tracing algorithm that can yield far superior results.

Given the current profusion of devices for viewing media, video content created at one aspect ratio is often viewed on displays with different aspect ratios. Many previous solutions address this problem by retargeting or resizing the video, but a more general solution would re-edit the video for the new display. Our method employs the three primary editing operations: pan, cut, and zoom. We let viewers implicitly reveal what is important in a video by tracking their gaze as they watch the video. We present an algorithm that optimizes the path of a cropping window based on the collected eyetracking data, finds places to cut, and computes the size of the cropping window. We present results on a variety of video clips, including close-up and distant shots, and stationary and moving cameras. We conduct two experiments to evaluate our results. First, we eyetrack viewers on the result videos generated by our algorithm, and second, we perform a subjective assessment of viewer preference. These experiments show that viewer gaze patterns are similar on our result videos and on the original video clips, and that viewers prefer our results to an optimized crop-and-warp algorithm.

We present a semi-automatic approach to exchange the clothes of an actor for arbitrary virtual garments in conventional monocular video footage as a postprocess. We reconstruct the actor's body shape and motion from the input video using a parameterized body model. The reconstructed dynamic 3D geometry of the actor serves as an animated mannequin for simulating the virtual garment. It also aids in scene illumination estimation, necessary to realistically light the virtual garment. An image-based warping technique ensures realistic compositing of the rendered virtual garment and the original video. We present results for eight real-world video sequences featuring complex test cases to evaluate performance for different types of motion, camera settings, and illumination conditions.

We present an interactive simulation framework for authoring surgical procedures of soft tissue manipulation using physics-based simulation to animate the flesh. This interactive authoring tool can be used by clinical educators to craft three-dimensional illustrations of the intricate maneuvers involved in craniofacial repairs, in contrast to two-dimensional sketches and still photographs which are the medium used to describe these procedures in the traditional surgical curriculum. Our virtual environment also allows surgeons-intraining to develop cognitive skills for craniofacial surgery by experimenting with different approaches to reconstructive challenges, adapting stock techniques to flesh regions with nonstandard shape, and reach preliminary predictions about the feasibility of a given repair plan. We use a Cartesian grid-based embedded discretization of nonlinear elasticity to maximize regularity, and expose opportunities for aggressive multithreading and SIMD accelerations. Using a grid-based approach facilitates performance and scalability, but constrains our ability to capture the topology of thin surgical incisions. We circumvent this restriction by hybridizing the grid-based discretization with an explicit hexahedral mesh representation in regions where the embedding mesh necessitates overlap or nonmanifold connectivity. Finally, we detail how the front-end of our system can run on lightweight clients, while the core simulation capability can be hosted on a dedicated server and delivered as a network service.

We introduce the foldabilization problem for space-saving furniture design. Namely, given a 3D object representing a piece of furniture, our goal is to apply a minimum amount of modification to the object so that it can be folded to save space --- the object is thus foldabilized. We focus on one instance of the problem where folding is with respect to a prescribed folding direction and allowed object modifications include hinge insertion and part shrinking. We develop an automatic algorithm for foldabilization by formulating and solving a nested optimization problem operating at two granularity levels of the input shape. Specifically, the input shape is first partitioned into a set of integral folding units. For each unit, we construct a graph which encodes conflict relations, e.g., collisions, between foldings implied by various patch foldabilizations within the unit. Finding a minimum-cost foldabilization with a conflict-free folding is an instance of the maximum-weight independent set problem. In the outer loop of the optimization, we process the folding units in an optimized ordering where the units are sorted based on estimated foldabilization costs. We show numerous foldabilization results computed at interactive speed and 3D-print physical prototypes of these results to demonstrate manufacturability.

We present a novel explicit surface tracking method. Its main advantage over existing approaches is the fact that it is both completely grid-free and fast which makes it ideal for the use in large unbounded domains. A further advantage is that its running time is less sensitive to temporal variations of the input mesh than existing approaches. In terms of performance, the method provides a good trade-off point between speed and quality. The main idea behind our approach to handle topological changes is to delete all overlapping triangles and to fill or join the resulting holes in a robust and efficient way while guaranteeing that the output mesh is both manifold and without boundary. We demonstrate the flexibility, speed and quality of our method in various applications such as Eulerian and Lagrangian liquid simulations and the simulation of solids under large plastic deformations.

We address the problem of allowing casual users to customize parametric models while maintaining their valid state as 3D-printable functional objects. We define Fab Form as any design representation that lends itself to interactive customization by a novice user, while remaining valid and manufacturable. We propose a method to achieve these Fab Form requirements for general parametric designs tagged with a general set of automated validity tests and a small number of parameters exposed to the casual user. Our solution separates Fab Form evaluation into a precomputation stage and a runtime stage. Parts of the geometry and design validity (such as manufacturability) are evaluated and stored in the precomputation stage by adaptively sampling the design space. At runtime the remainder of the evaluation is performed. This allows interactive navigation in the valid regions of the design space using an automatically generated Web user interface (UI). We evaluate our approach by converting several parametric models into corresponding Fab Forms.

The human perception of stylistic similarity transcends structure and function: for instance, a bed and a dresser may share a common style. An algorithmically computed style similarity measure that mimics human perception can benefit a range of computer graphics applications. Previous work in style analysis focused on shapes within the same class, and leveraged structural similarity between these shapes to facilitate analysis. In contrast, we introduce the firststructure-transcendingstyle similarity measure and validate it to be well aligned with human perception of stylistic similarity. Our measure is inspired by observations about style similarity in art history literature, which point to the presence of similarly shaped, salient,geometric elementsas one of the key indicators of stylistic similarity. We translate these observations into an algorithmic measure by first quantifying the geometric properties that make humans perceive geometric elements as similarly shaped and salient in the context of style, then employing this quantification to detect pairs of matching style related elements on the analyzed models, and finally collating the element-level geometric similarity measurements into an object-level style measure consistent with human perception. To achieve this consistency we employ crowdsourcing to quantify the different components of our measure; we learn the relative perceptual importance of a range of elementary shape distances and other parameters used in our measurement from 50K responses to cross-structure style similarity queries provided by over 2500 participants.We train and validate our method on this dataset, showing it to successfully predict relative style similarity with near 90% accuracy based on 10-fold cross-validation.

We introduce elastic textures: a set of parametric, tileable, printable, cubic patterns achieving a broad range of isotropic elastic material properties: the softest pattern is over a thousand times softer than the stiffest, and the Poisson's ratios range from below zero to nearly 0.5. Using a combinatorial search over topologies followed by shape optimization, we explore a wide space of truss-like, symmetric 3D patterns to obtain a small family. This pattern family can be printed without internal support structure on a single-material 3D printer and can be used to fabricate objects with prescribed mechanical behavior. The family can be extended easily to create anisotropic patterns with target orthotropic properties. We demonstrate that our elastic textures are able to achieve a user-supplied varying material property distribution. We also present a material optimization algorithm to choose material properties at each point within an object to best fit a target deformation under a prescribed scenario. We show that, by fabricating these spatially varying materials with elastic textures, the desired behavior is achieved.

The locomotion skills developed for physics-based characters most often target flat terrain. However, much of their potential lies with the creation of dynamic, momentum-based motions across more complex terrains. In this paper, we learn controllers that allow simulated characters to traverse terrains with gaps, steps, and walls using highly dynamic gaits. This is achieved using reinforcement learning, with careful attention given to the action representation, non-parametric approximation of both the value function and the policy; epsilon-greedy exploration; and the learning of a good state distance metric. The methods enable a 21-link planar dog and a 7-link planar biped to navigate challenging sequences of terrain using bounding and running gaits. We evaluate the impact of the key features of our skill learning pipeline on the resulting performance.

We present a complete pipeline for creating fully rigged, personalized 3D facial avatars from hand-held video. Our system faithfully recovers facial expression dynamics of the user by adapting a blendshape template to an image sequence of recorded expressions using an optimization that integrates feature tracking, optical flow, and shape from shading. Fine-scale details such as wrinkles are captured separately in normal maps and ambient occlusion maps. From this user- and expression-specific data, we learn a regressor for on-the-fly detail synthesis during animation to enhance the perceptual realism of the avatars. Our system demonstrates that the use of appropriate reconstruction priors yields compelling face rigs even with a minimalistic acquisition system and limited user assistance. This facilitates a range of new applications in computer animation and consumer-level online communication based on personalized avatars. We present realtime application demos to validate our method.