SIGGRAPH 2019 论文列表

We present X-shells, a new class of deployable structures formed by an ensemble of elastically deforming beams coupled through rotational joints. An X-shell can be assembled conveniently in a flat configuration from standard elastic beam elements and then deployed through force actuation into the desired 3D target state. During deployment, the coupling imposed by the joints will force the beams to twist and buckle out of plane to maintain a state of static equilibrium. This complex interaction of discrete joints and continuously deforming beams allows interesting 3D forms to emerge. Simulating X-shells is challenging, however, due to unstable equilibria at the onset of beam buckling. We propose an optimization-based simulation framework building on a discrete rod model that robustly handles such difficult scenarios by analyzing and appropriately modifying the elastic energy Hessian. This real-time simulation method forms the basis of a computational design tool for X-shells that enables interactive design space exploration by varying and optimizing design parameters to achieve a specific design intent. We jointly optimize the assembly state and the deployed configuration to ensure the geometric and structural integrity of the deployable X-shell. Once a design is finalized, we also optimize for a sparse distribution of actuation forces to efficiently deploy it from its flat assembly state to its 3D target state. We demonstrate the effectiveness of our design approach with a number of design studies that highlight the richness of the X-shell design space, enabling new forms not possible with existing approaches. We validate our computational model with several physical prototypes that show excellent agreement with the optimized digital models.

We study discrete geodesic foliations of surfaces---foliations whose leaves are all approximately geodesic curves---and develop several new variational algorithms for computing such foliations. Our key insight is a relaxation of vector field integrability in the discrete setting, which allows us to optimize for curl-free unit vector fields that remain well-defined near singularities and robustly recover a scalar function whose gradient is well aligned to these fields. We then connect the physics governing surfaces woven out of thin ribbons to the geometry of geodesic foliations, and present a design and fabrication pipeline for approximating surfaces of arbitrary geometry and topology by triaxially-woven structures, where the ribbon layout is determined by a geodesic foliation on a sixfold branched cover of the input surface. We validate the effectiveness of our pipeline on a variety of simulated and fabricated woven designs, including an example for readers to try at home.

Common mechanical actuators for haptic feedback are generally dedicated to creating single kind of feedback, e.g., vibrotactile only, the pressure only, or shear force only, [Choi and Kuchenbecker 2013; Girard et al. 2016; Pacchierotti et al. 2017]. This is against the fact that highly realistic fully immersive VR/AR sometimes requires rather complete and rich multi-mode haptic feedback. For instance, when rubbing your finger on a wooden desk, the fingertip simultaneously senses both the high-frequency vibration due to the roughness of the surface texture and the quasi-static pressure due to pushing force, and your brain combines them to feel it as a wooden desk. The lack of any of the involved physical signal may seriously deteriorate the realism. This may be one of the reasons why current haptic interface technology for VR/AR environments is not at the same level as visual interfaces.

Imaging objects outside a camera's direct line of sight has important applications in robotic vision, remote sensing, and many other domains. Time-of-flight-based non-line-of-sight (NLOS) imaging systems have recently demonstrated impressive results, but several challenges remain. Image formation and inversion models have been slow or limited by the types of hidden surfaces that can be imaged. Moreover, non-planar sampling surfaces and non-confocal scanning methods have not been supported by efficient NLOS algorithms. With this work, we introduce a wave-based image formation model for the problem of NLOS imaging. Inspired by inverse methods used in seismology, we adapt a frequency-domain method, f-k migration, for solving the inverse NLOS problem. Unlike existing NLOS algorithms, f-k migration is both fast and memory efficient, it is robust to specular and other complex reflectance properties, and we show how it can be used with non-confocally scanned measurements as well as for non-planar sampling surfaces. f-k migration is more robust to measurement noise than alternative methods, generally produces better quality reconstructions, and is easy to implement. We experimentally validate our algorithms with a new NLOS imaging system that records room-sized scenes outdoors under indirect sunlight, and scans persons wearing retroreflective clothing at interactive rates.

Computed tomography has emerged as the method of choice for scanning complex shapes as well as interior structures of stationary objects. Recent progress has also allowed the use of CT for analyzing deforming objects and dynamic phenomena, although the deformations have been constrained to be either slow or periodic motions. In this work we improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations. Our method uses a warp-and-project approach that allows us to introduce an essentially continuous time axis where consistency of the reconstructed shape with the projection images is enforced for the specific time and deformation state at which the image was captured. The method uses an efficient, time-adaptive solver that yields both the moving geometry as well as the deformation field. We validate our method with extensive experiments using both synthetic and real data from a range of different application scenarios.

Despite recent developments towards on-demand, individualized garment design and fabrication, the majority of processes in the fashion industry are still inefficient and heavily dependent on manual work. A significant amount of recent research in this area has been focused on supporting designers to digitally create sewing patterns and shapes, but there is little work on textured fabrics. Aligning textile patterns like stripes or plaid along garment seams requires an experienced tailor and is thus reserved only for expensive, high-end garments. We present an interactive algorithm for automatically aligning repetitive textile patterns along seams for a given garment, allowing a user to make design choices at each step of our pipeline. Our approach is based on the 17 wallpaper groups and the symmetries they exhibit. We exploit these symmetries to optimize the alignment of the sewing pattern with the textured fabric for each of its pieces, determining where to cut the fabric. We optionally alter the sewing pattern slightly for a perfect fit along seams, without visibly changing the 3D shape of the garment. The pieces can then be cut automatically by a CNC or laser cutter. Our approach fits within the pipeline of digital garment design, eliminating the difficult, manual step of aligning and cutting the garment pieces by hand.

A procedural art-directable workflow is developed for voxel 3D printing using existing digital effects technologies. Customised for the Stratasys J750's unique materials, the system produces large-scale prosthetic eyes as a case study for film and display work.

We present Vox-Cells, a volume visualization framework designed for real-time volume investigation and exploration. We seek to treat data as a first-class citizen with a 1:1 relationship between the data and its corresponding representation. CPU-GPU transfer is minimized, and novel approaches to volume construction and lighting are explored in order to maximize performance for deployment on consumer grade Virtual Reality (VR) Head-mounted displays (HMD).

We propose a novel technique for the automatic design of molds to cast highly complex shapes. The technique generates composite, two-piece molds. Each mold piece is made up of a hard plastic shell and a flexible silicone part. Thanks to the thin, soft, and smartly shaped silicone part, which is kept in place by a hard plastic shell, we can cast objects of unprecedented complexity. An innovative algorithm based on a volumetric analysis defines the layout of the internal cuts in the silicone mold part. Our approach can robustly handle thin protruding features and intertwined topologies that have caused previous methods to fail. We compare our results with state of the art techniques, and we demonstrate the casting of shapes with extremely complex geometry.

The efficiency of Monte Carlo methods, commonly used to render participating media, is directly linked to the manner in which random sampling decisions are made during path construction. Notably, path construction is influenced by scattering direction and distance sampling, Russian roulette, and splitting strategies. We present a consistent suite of volumetric path construction techniques where all these sampling decisions are guided by a cached estimate of the adjoint transport solution. The proposed strategy is based on the theory of zero-variance path sampling schemes, accounting for the spatial and directional variation in volumetric transport. Our key technical contribution, enabling the use of this approach in the context of volume light transport, is a novel guiding strategy for sampling the particle collision distance proportionally to the product of transmittance and the adjoint transport solution (e.g., in-scattered radiance). Furthermore, scattering directions are likewise sampled according to the product of the phase function and the incident radiance estimate. Combined with guided Russian roulette and splitting strategies tailored to volumes, we demonstrate about an order-of-magnitude error reduction compared to standard unidirectional methods. Consequently, our approach can render scenes otherwise intractable for such methods, while still retaining their simplicity (compared to, e.g., bidirectional methods).

Plasmonic color generation describes structural color arising from resonant interaction between visible light and metallic nanostructures, causing selective frequencies of light to be scattered and/or absorbed [Kristensen et al. 2017; Sun and Xia 2003]. The perceived color from such metallic nanostructures is highly dependent on viewing angle and the color appearance can change with color of the viewing background. Plasmonic color generation is a rapidly emerging research area with potential advantages over conventional pigment printing technology including higher printing resolution and robustness, greater compatibility for integration and functionalization, and reduced resource requirements [Mudachathi and Tanaka 2017; Zhu et al. 2017]. Structural color from plasmonic nanostructures has already been used to improve security measures in currency notes and credit cards [Lee et al. 2018].

Representing smooth geometric shapes by polyhedral meshes can be quite difficult in situations where the variation of edges and face normals is prominently visible. Especially problematic are saddle-shaped areas of the mesh, where typical vertices with six incident edges are ill suited to emulate the more symmetric smooth situation. The importance of a faithful discrete representation is apparent for certain special applications like freeform architecture, but is also relevant for simulation and geometric computing. In this paper we discuss what exactly is meant by a good representation of saddle points, and how this requirement is stronger than a good approximation of a surface plus its normals. We characterize good saddles in terms of the normal pyramid in a vertex. We show several ways to design meshes whose normals enjoy small variation (implying good saddle points). For this purpose we define a discrete energy of polyhedral surfaces, which is related to a certain total absolute curvature of smooth surfaces. We discuss the minimizers of both functionals and in particular show that the discrete energy is minimal not for triangle meshes, but for principal quad meshes. We demonstrate our procedures for optimization and interactive design by means of meshes intended for architectural design.

Visual effects (called "VFX" in this poster) have expanded the range of expression and been necessary to enhance the mood for the story in films and videos. A variety of software tools which support producing VFX has been developed.

Industrial knitting machines are commonly used to manufacture complicated shapes from yarns; however, designing patterns for these machines requires extensive training. We present the first general visual programming interface for creating 3D objects with complex surface finishes on industrial knitting machines. At the core of our interface is a new, augmented, version of the stitch mesh data structure. The augmented stitch mesh stores low-level knitting operations per-face and encodes the dependencies between faces using directed edge labels. Our system can generate knittable augmented stitch meshes from 3D models, allows users to edit these meshes in a way that preserves their knittability, and can schedule the execution order and location of each face for production on a knitting machine. Our system is general, in that its knittability-preserving editing operations are sufficient to transform between any two machine-knittable stitch patterns with the same orientation on the same surface. We demonstrate the power and flexibility of our pipeline by using it to create and knit objects featuring a wide range of patterns and textures, including intarsia and Fair Isle colorwork; knit and purl textures; cable patterns; and laces.

Strokes have a wide range of occurrence among people of all races and genders. The consequences of a stroke often include significant muscle weakness on one side of the body that must be physically exercised to attempt to restore its previous strength and mobility. In traditional mirror therapy, the partially disabled hand or leg is hidden by a mirror. The patient sees a reflection of the healthy side of their body where the disabled limb should be, in order to stimulate brain to operate the partially disabled hand/leg in the proper way. The patient will interact with mirror using unaffected hand and observe its reflection.

We present Vidgets, a family of mechanical widgets, specifically push buttons and rotary knobs that augment mobile devices with tangible user interfaces. When these widgets are attached to a mobile device and a user interacts with them, the widgets' nonlinear mechanical response shifts the device slightly and quickly, and this subtle motion can be detected by the accelerometer commonly equipped on mobile devices. We propose a physics-based model to understand the nonlinear mechanical response of widgets. This understanding enables us to design tactile force profiles of these widgets so that the resulting accelerometer signals become easy to recognize. We then develop a lightweight signal processing algorithm that analyzes the accelerometer signals and recognizes how the user interacts with the widgets in real time. Vidgets widgets are low-cost, compact, reconfigurable, and power efficient. They can form a diverse set of physical interfaces that enrich users' interactions with mobile devices in various practical scenarios. We demonstrate their use in three applications: photo capture with single-handed zoom, control of mobile games, and making a playable mobile music instrument.

With the popularity of immersive display systems that fill the viewer’s field of view (FOV) entirely, demand for wide FOV content has increased. A video extrapolation technique based on reuse of existing videos is one of the most efficient ways to produce wide FOV content. Extrapolating a video poses a great challenge, however, due to the insufficient amount of cues and information that can be leveraged for the estimation of the extended region. This article introduces a novel framework that allows the extrapolation of an input video and consequently converts a conventional content into one with wide FOV. The key idea of the proposed approach is to integrate the information from all frames in the input video into each frame. Utilizing the information from all frames is crucial because it is very difficult to achieve the goal with a two-dimensional transformation based approach when parallax caused by camera motion is apparent. Warping guided by three-dimensnional scene points matches the viewpoints between the different frames. The matched frames are blended to create extended views. Various experiments demonstrate that the results of the proposed method are more visually plausible than those produced using state-of-the-art techniques.

Creating animations for robotic characters is very challenging due to the constraints imposed by their physical nature. In particular, the combination of fast motions and unavoidable structural deformations leads to mechanical oscillations that negatively affect their performances. Our goal is to automatically transfer motions created using traditional animation software to robotic characters while avoiding such artifacts. To this end, we develop an optimization-based, dynamics-aware motion retargeting system that adjusts an input motion such that visually salient low-frequency, large amplitude vibrations are suppressed. The technical core of our animation system consists of a differentiable dynamics simulator that provides constraint-based two-way coupling between rigid and flexible components. We demonstrate the efficacy of our method through experiments performed on a total of five robotic characters including a child-sized animatronic figure that features highly dynamic drumming and boxing motions.