ABSTRACT Compositing human figures into scene images has broad applications in areas such as entertainment and advertising. However, existing methods often cannot handle occlusion of the inserted person by foreground objects and unnaturally place the person in the frontmost layer. Moreover, they offer limited control over the inserted person's pose. To address these challenges, we propose two methods. Both allow explicit pose control via a 3D body model and leverage latent diffusion models to synthesize the person at a contextually appropriate depth, naturally handling occlusions without requiring occlusion masks. The first is a two‐stage approach: the model first learns a depth map of the scene with the person through supervised learning, and then synthesizes the person accordingly. The second method learns occlusion implicitly and synthesizes the person directly from input data without explicit depth supervision. Quantitative and qualitative evaluations show that both methods outperform existing approaches by better preserving scene consistency while accurately reflecting occlusions and user‐specified poses.

Recovering high-quality 3D facial textures from single-view 2D images is a challenging task, especially under the constraints of limited data and complex facial details such as wrinkles, makeup, and occlusions. In this paper, we introduce FreeUV, a novel ground-truth-free UV texture recovery framework that eliminates the need for annotated or synthetic UV data. FreeUV leverages a pre-trained stable diffusion model alongside a Cross-Assembly inference strategy to fulfill this objective. In FreeUV, separate networks are trained independently to focus on realistic appearance and structural consistency, and these networks are combined during inference to generate coherent textures. Our approach accurately captures intricate facial features and demonstrates robust performance across diverse poses and occlusions. Extensive experiments validate FreeUV’s effectiveness, with results surpassing state-of-the-art methods in both quantitative and qualitative metrics. Additionally, FreeUV enables new applications, including local editing, facial feature interpolation, and texture recovery from multi-view images. By reducing data requirements, FreeUV offers a scalable solution for generating high-fidelity 3D facial textures suitable for real-world scenarios.

Achieving aesthetically pleasing photography necessitates attention to multiple factors, including composition and capture conditions, which pose challenges to novices. Prior research has explored the enhancement of photo aesthetics post-capture through 2D manipulation techniques; however, these approaches offer limited search space for aesthetics. We introduce a pioneering method that employs 3D operations to simulate the conditions at the moment of capture retrospectively. Our approach extrapolates the input image and then reconstructs the 3D scene from the extrapolated image, followed by optimization to find camera parameters and image size that yield the best 3D view with enhanced aesthetics. Comparative qualitative and quantitative assessments reveal that our method surpasses traditional 2D editing techniques with superior aesthetics.

Abstract Relighting of human images enables post‐photography editing of lighting effects in portraits. The current mainstream approach uses neural networks to approximate lighting effects without explicitly accounting for the principle of physical shading. As a result, it often has difficulty representing high‐frequency shadows and shading. In this paper, we propose a two‐stage relighting method that can reproduce physically‐based shadows and shading from low to high frequencies. The key idea is to approximate an environment light source with a set of a fixed number of area light sources. The first stage employs supervised inverse rendering from a single image using neural networks and calculates physically‐based shading. The second stage then calculates shadow for each area light and sums up to render the final image. We propose to make soft shadow mapping differentiable for the area‐light approximation of environment lighting. We demonstrate that our method can plausibly reproduce all‐frequency shadows and shading caused by environment illumination, which have been difficult to reproduce using existing methods.

Three-dimensional terrain models are essential in domains such as video game development and film production. Because surface color is often correlated with terrain geometry, capturing this relationship is critical for generating realistic results. However, most existing methods synthesize either a heightmap or a texture without adequately modeling their inherent correlation. We propose a method that jointly generates terrain heightmaps and textures using a latent diffusion model. First, we train the model in an unsupervised manner to randomly generate paired heightmaps and textures. Then, we perform supervised learning on an external adapter to enable user control via hand-drawn sketches. Experiments demonstrate that our approach supports intuitive terrain generation while preserving the correlation between heightmaps and textures. Our method outperforms the two-stage and GAN-based baselines by ensuring structural coherence, in which textures naturally align with geometry, successfully accommodating both realistic landscapes and extreme user-defined shapes.

In this work, we introduce two types of makeup prior models to extend existing 3D face prior models: PCA-based and StyleGAN2-based priors. The PCA-based prior model is a linear model that is easy to construct and is computationally efficient. However, it retains only low-frequency information. Conversely, the StyleGAN2-based model can represent high-frequency information with relatively higher computational cost than the PCA-based model. Although there is a trade-off between the two models, both are applicable to 3D facial makeup estimation and related applications. By leveraging makeup prior models and designing a makeup consistency module, we effectively address the challenges that previous methods faced in robustly estimating makeup, particularly in the context of handling self-occluded faces. In experiments, we demonstrate that our approach reduces computational costs by several orders of magnitude, achieving speeds up to 180 times faster. In addition, by improving the accuracy of the estimated makeup, we confirm that our methods are highly advantageous for various 3D facial makeup applications such as 3D makeup face reconstruction, user-friendly makeup editing, makeup transfer, and interpolation.

Pose and body shape editing in a human image has received increasing attention. However, current methods often struggle with dataset biases and deteriorate realism and the person’s identity when users make large edits. We propose a one-shot approach that enables large edits with identity preservation. To enable large edits, we fit a 3D body model, project the input image onto the 3D model, and change the body’s pose and shape. Because this initial textured body model has artifacts due to occlusion and the inaccurate body shape, the rendered image undergoes a diffusion-based refinement, in which strong noise destroys body structure and identity whereas insufficient noise does not help. We thus propose an iterative refinement with weak noise, applied first for the whole body and then for the face. We further enhance the realism by fine-tuning text embeddings via self-supervised learning. Our quantitative and qualitative evaluations demonstrate that our method outperforms other existing methods across various datasets.

Abstract Facial makeup enriches the beauty of not only real humans but also virtual characters; therefore, makeup for 3D facial models is highly in demand in productions. However, painting directly on 3D faces and capturing real‐world makeup are costly, and extracting makeup from 2D images often struggles with shading effects and occlusions. This paper presents the first method for extracting makeup for 3D facial models from a single makeup portrait. Our method consists of the following three steps. First, we exploit the strong prior of 3D morphable models via regression‐based inverse rendering to extract coarse materials such as geometry and diffuse/specular albedos that are represented in the UV space. Second, we refine the coarse materials, which may have missing pixels due to occlusions. We apply inpainting and optimization. Finally, we extract the bare skin, makeup, and an alpha matte from the diffuse albedo. Our method offers various applications for not only 3D facial models but also 2D portrait images. The extracted makeup is well‐aligned in the UV space, from which we build a large‐scale makeup dataset and a parametric makeup model for 3D faces. Our disentangled materials also yield robust makeup transfer and illumination‐aware makeup interpolation/removal without a reference image.

Abstract This paper tackles the challenging problem of one‐shot semantic image synthesis from rough sparse annotations, which we call “semantic scribbles.” Namely, from only a single training pair annotated with semantic scribbles, we generate realistic and diverse images with layout control over, for example, facial part layouts and body poses. We present a training strategy that performs pseudo labeling for semantic scribbles using the StyleGAN prior. Our key idea is to construct a simple mapping between StyleGAN features and each semantic class from a single example of semantic scribbles. With such mappings, we can generate an unlimited number of pseudo semantic scribbles from random noise to train an encoder for controlling a pretrained StyleGAN generator. Even with our rough pseudo semantic scribbles obtained via one‐shot supervision, our method can synthesize high‐quality images thanks to our GAN inversion framework. We further offer optimization‐based postprocessing to refine the pixel alignment of synthesized images. Qualitative and quantitative results on various datasets demonstrate improvement over previous approaches in one‐shot settings.

Abstract This article presents the first technique to estimate a 3D terrain model from a single landscape image. Although monocular depth estimation also offers single‐image 3D reconstruction, it assigns depth only to pixels visible in the input image, resulting in an incomplete 3D terrain output. Our method generates a complete 3D terrain model as a textured height map via a three‐stage framework using deep neural networks. First, to exploit the performance of pixel‐aligned estimation, we estimate terrain's per‐pixel depth and color free from shadows or lights in the perspective view. Second, we triangulate the RGB‐D data generated in the first stage and rasterize the triangular mesh from the top view to obtain an incomplete textured height map. Finally, we inpaint the depth and color in the missing regions. Because there are many possible ways to complete the missing regions, we synthesize diverse shapes and textures during inpainting using a variational autoencoder. Qualitative and quantitative experiments reveal that our method outperforms existing methods applying a direct perspective‐to‐top view transform as image‐to‐image translation.

Origami has provided a potential way to construct 3D curved structures by folding flat sheet materials without cutting or stretching. As a traditional origami, waterbomb tessellation is widely studied from aspects of science and engineering. However, users cannot easily utilize this kind of origami to fit curved target surfaces because the underlying geometric constraints limit the design space. In this study, we propose computational design methods for approximating cylindrical and axisymmetric curved surfaces based on waterbomb tessellations. With consideration of symmetry and periodic repetition, a single strip of the waterbomb tessellation is first modeled and then longitudinally and circumferentially replicated to construct cylindrical and axisymmetric waterbomb tessellations, respectively. To fulfill flat-foldability, an optimization process is introduced for minimizing flat-foldable residuals iteratively and then a regulation process of the crease pattern is applied for further reducing such residuals. In addition, we demonstrate waterbomb-derivative tessellations with quad-paddings to expand the design variations. Furthermore, rigid-folding sequences and several physically engineered origami pieces are presented. The proposed methods can be utilized to facilitate the design of origami-inspired structures for various engineering design purposes, such as foldable shelters, tubular structures, metamaterials, and so on.

Abstract The modern supervised approaches for human image relighting rely on training data generated from 3D human models. However, such datasets are often small (e.g., Light Stage data with a small number of individuals) or limited to diffuse materials (e.g., commercial 3D scanned human models). Thus, the human relighting techniques suffer from the poor generalization capability and synthetic‐to‐real domain gap. In this paper, we propose a two‐stage method for single‐image human relighting with domain adaptation. In the first stage, we train a neural network for diffuse‐only relighting. In the second stage, we train another network for enhancing non‐diffuse reflection by learning residuals between real photos and images reconstructed by the diffuse‐only network. Thanks to the second stage, we can achieve higher generalization capability against various cloth textures, while reducing the domain gap. Furthermore, to handle input videos, we integrate illumination‐aware deep video prior to greatly reduce flickering artifacts even with challenging settings under dynamic illuminations.

Abstract Semantic image synthesis is a process for generating photorealistic images from a single semantic mask. To enrich the diversity of multimodal image synthesis, previous methods have controlled the global appearance of an output image by learning a single latent space. However, a single latent code is often insufficient for capturing various object styles because object appearance depends on multiple factors. To handle individual factors that determine object styles, we propose a class‐ and layer‐wise extension to the variational autoencoder (VAE) framework that allows flexible control over each object class at the local to global levels by learning multiple latent spaces. Furthermore, we demonstrate that our method generates images that are both plausible and more diverse compared to state‐of‐the‐art methods via extensive experiments with real and synthetic datasets in three different domains. We also show that our method enables a wide range of applications in image synthesis and editing tasks.

Abstract Modeling 3D origami pieces using conventional software is laborious due to the geometric constraints imposed by the complicated layered structure. Targeting origami models used in visual content such as CG illustrations and movies, we propose an interactive system that dramatically simplifies the modeling of 3D origami pieces with plausible outer shapes, while omitting accurate inner structures. By focusing on flat origami models with a front‐and‐back symmetry commonly found in traditional artworks, our system realizes easy and quick modeling via single‐view interface; given a reference image of the target origami piece, the user draws polygons of planar faces onto the image, and assigns annotations indicating the types of folding operations. Our system automatically rectifies the manually‐specified polygons, infers the folded structures that should yield the user‐specified polygons with reference to the depth order of layered polygons, and generates a plausible 3D model while accounting for gaps between layers. Our system is versatile enough for modeling pseudo‐origami models that are not realizable by folding a single sheet of paper. Our user study demonstrates that even novice users without the specialized knowledge and experience on origami and 3D modeling can create plausible origami models quickly.

Automatic generation of a high-quality video from a single image remains a challenging task despite the recent advances in deep generative models. This paper proposes a method that can create a high-resolution, long-term animation using convolutional neural networks (CNNs) from a single landscape image where we mainly focus on skies and waters. Our key observation is that the motion (e.g., moving clouds) and appearance (e.g., time-varying colors in the sky) in natural scenes have different time scales. We thus learn them separately and predict them with decoupled control while handling future uncertainty in both predictions by introducing latent codes. Unlike previous methods that infer output frames directly, our CNNs predict spatially-smooth intermediate data, i.e., for motion, flow fields for warping, and for appearance, color transfer maps, via self-supervised learning, i.e., without explicitly-provided ground truth. These intermediate data are applied not to each previous output frame, but to the input image only once for each output frame. This design is crucial to alleviate error accumulation in long-term predictions, which is the essential problem in previous recurrent approaches. The output frames can be looped like cinemagraph, and also be controlled directly by specifying latent codes or indirectly via visual annotations. We demonstrate the effectiveness of our method through comparisons with the state-of-the-arts on video prediction as well as appearance manipulation. Resultant videos, codes, and datasets will be available at http://www.cgg.cs.tsukuba.ac.jp/~endo/projects/AnimatingLandscape.

Parsing of human images is a fundamental task for determining semantic parts such as the face, arms, and legs, as well as a hat or a dress. Recent deep-learning-based methods have achieved significant improvements, but collecting training datasets with pixel-wise annotations is labor-intensive. In this paper, we propose two solutions to cope with limited datasets. Firstly, to handle various poses, we incorporate a pose estimation network into an end-to-end human-image parsing network, in order to transfer common features across the domains. The pose estimation network can be trained using rich datasets and can feed valuable features to the human-image parsing network. Secondly, to handle complicated backgrounds, we increase the variation in image backgrounds automatically by replacing the original backgrounds of human images with others obtained from large-scale scenery image datasets. Individually, each solution is versatile and beneficial to human-image parsing, while their combination yields further improvement. We demonstrate the effectiveness of our approach through comparisons and various applications such as garment recoloring, garment texture transfer, and visualization for fashion analysis.

Relighting of human images has various applications in image synthesis. For relighting, we must infer albedo, shape, and illumination from a human portrait. Previous techniques rely on human faces for this inference, based on spherical harmonics (SH) lighting. However, because they often ignore light occlusion, inferred shapes are biased and relit images are unnaturally bright particularly at hollowed regions such as armpits, crotches, or garment wrinkles. This paper introduces the first attempt to infer light occlusion in the SH formulation directly. Based on supervised learning using convolutional neural networks (CNNs), we infer not only an albedo map, illumination but also a light transport map that encodes occlusion as nine SH coefficients per pixel. The main difficulty in this inference is the lack of training datasets compared to unlimited variations of human portraits. Surprisingly, geometric information including occlusion can be inferred plausibly even with a small dataset of synthesized human figures, by carefully preparing the dataset so that the CNNs can exploit the data coherency. Our method accomplishes more realistic relighting than the occlusion-ignored formulation.

Abstract This paper presents a method that can convert a given 3D mesh into a flat‐foldable model consisting of rigid panels. A previous work proposed a method to assist manual design of a single component of such flat‐foldable model, consisting of vertically‐connected side panels as well as horizontal top and bottom panels. Our method semi‐automatically generates a more complicated model that approximates the input mesh with multiple convex components. The user specifies the folding direction of each convex component and the fidelity of shape approximation. Given the user inputs, our method optimizes shapes and positions of panels of each convex component in order to make the whole model flat‐foldable. The user can check a folding animation of the output model. We demonstrate the effectiveness of our method by fabricating physical paper prototypes of flat‐foldable models.

Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images ) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.

Abstract Edit propagation is a technique that can propagate various image edits (e.g., colorization and recoloring) performed via user strokes to the entire image based on similarity of image features. In most previous work, users must manually determine the importance of each image feature (e.g., color, coordinates, and textures) in accordance with their needs and target images. We focus on representation learning that automatically learns feature representations only from user strokes in a single image instead of tuning existing features manually. To this end, this paper proposes an edit propagation method using a deep neural network (DNN). Our DNN, which consists of several layers such as convolutional layers and a feature combiner, extracts stroke‐adapted visual features and spatial features, and then adjusts the importance of them. We also develop a learning algorithm for our DNN that does not suffer from the vanishing gradient problem, and hence avoids falling into undesirable locally optimal solutions. We demonstrate that edit propagation with deep features, without manual feature tuning, can achieve better results than previous work.

Abstract 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.

Abstract Particle‐based liquid is often rendered only with single refraction in real‐time applications, which deteriorates the reality of liquid. We present a screen‐space method for rendering particle‐based liquids with up to four refractions in real time. Our method separates liquid particles into splashes and aggregations (i.e., liquid bodies) and generates a pair of depth maps of front‐facing and back‐facing surfaces for each. We use the depth maps of splashes as they are but smooth those of aggregations to reduce small bumps. For smoothing depth, we iteratively fit planes locally using moving least squares, unlike previous filtering‐based approaches that cause undesirable refractions around depth boundaries. By calculating refractions with physically based light attenuation, our method can render liquids more realistically than previous methods. Copyright © 2016 John Wiley & Sons, Ltd.

We propose an image-based virtual fitting system to reproduce the appearance of fitting during online shopping for garments. Inputs are whole-body images of a fashion model and the customer. We create a garment image by cutting out the garment portion from the image of the fashion model. If the garment images were naïvely superimposed, the fitting result would look strange mainly because the shape of the garment will not match the body shape of the customer. In this paper, we therefore propose a method of reshaping the garment image based on human body shapes of the fashion model and the customer to make the fitting result more realistic. The body shape is automatically estimated from the contours of the human body, and can easily be retouched if necessary. The fitting result is refined further by automatic color correction with reference to the facial regions and a method of retouching parts that protrude from the rear of the garment image. We verified the effectiveness of our system through a user test.

Abstract We propose an image editing system for repositioning objects in a single image based on the perspective of the scene. In our system, an input image is transformed into a layer structure that is composed of object layers and a background layer, and then the scene depth is computed from the ground region that is specified by the user using a simple boundary line. The object size and order of overlapping are automatically determined during the reposition based on the scene depth. In addition, our system enables the user to move shadows along with objects naturally by extracting the shadow mattes using only a few user‐specified scribbles. Finally, we demonstrate the versatility of our system through applications to depth‐of‐field effects, fog synthesis and 3D walkthrough in an image.

Abstract In this paper, we propose an interactive technique for constructing a 3D scene via sparse user inputs. We represent a 3D scene in the form of a Layered Depth Image (LDI) which is composed of a foreground layer and a background layer, and each layer has a corresponding texture and depth map. Given user‐specified sparse depth inputs, depth maps are computed based on superpixels using interpolation with geodesic‐distance weighting and an optimization framework. This computation is done immediately, which allows the user to edit the LDI interactively. Additionally, our technique automatically estimates depth and texture in occluded regions using the depth discontinuity. In our interface, the user paints strokes on the 3D model directly. The drawn strokes serve as 3D handles with which the user can pull out or push the 3D surface easily and intuitively with real‐time feedback. We show our technique enables efficient modeling of LDI that produce sufficient 3D effects.

In hair simulation, hair collision handling plays an important role to make hair look realistic; hair collision maintains the volume of hair. Without hair collision, hair would appear unnaturally flat.

Abstract 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.

Abstract There is considerable recent progress in hair simulations, driven by the high demands in computer animated movies. However, capturing the complex interactions between hair and water is still relatively in its infancy. Such interactions are best modeled as those between water and an anisotropic permeable medium as water can flow into and out of the hair volume biased in hair fiber direction. Modeling the interaction is further challenged when the hair is allowed to move. In this paper, we introduce a simulation model that reproduces interactions between water and hair as a dynamic anisotropic permeable material. We utilize an Eulerian approach for capturing the microscopic porosity of hair and handle the wetting effects using a Cartesian bounding grid. A Lagrangian approach is used to simulate every single hair strand including interactions with each other, yielding fine‐detailed dynamic hair simulation. Our model and simulation generate many interesting effects of interactions between fine‐detailed dynamic hair and water, i.e., water absorption and diffusion, cohesion of wet hair strands, water flow within the hair volume, water dripping from the wet hair strands and morphological shape transformations of wet hair.

ABSTRACT String‐like objects in our daily lives, for example shoelaces, threads, rubber cords, plastic fiber and spaghetti, have a wide variety of materials. Such string‐like objects also exhibit interesting behaviors such as twisting, tearing (by stretching or twisting), and bouncing back when pulled and released. In this paper, we present a method that enables these behaviors and simulates such materials in traditional string simulation methods that explicitly represent a string by particles and segments. Specifically, we offer the following three contributions. First, we introduce a method for handling twisting effects with both uniform and non‐uniform torsional rigidities. Second, we propose a method for estimating the tension acting on inextensible strings in order to reproduce tearing and flicking (bouncing back), whereas the tension for anextensibleobject can be computed via stretched length. The length of aninextensibleobject is maintained constant in general, and thus, we need a novel approach. Third, we introduce an optimized grid‐based collision detection for accelerating the computation. We demonstrate that our method can produce visually plausible animations of string‐like objects with various material properties, and it is a fast framework for interactive applications such as games. Copyright © 2012 John Wiley & Sons, Ltd.

One-dimensional deformable objects or elastic rods are widely used to represent threads, hair strands and elastic ropes in computer graphics. Such an object has the bending, stretching and twisting behaviors which are difficult to handle. To simulate deformable objects, a simple method called Lattice Shape Matching (LSM) [Rivers and James 2007] was recently proposed. LSM can handle the deformable objects ranging from soft bodies to stiff objects with an unconditional stability. For one-dimensional deformable objects, [Rungjiratananon et al. 2010] introduced a simplified model of LSM called Chain Shape Matching (CSM), which represents the one-dimensional deformable object by a chain of particles connected by segments. However, LSM and CSM can capture only the bending and stretching behaviors, but cannot handle twisting.

Abstract This paper presents a novel framework for elliptical weighted average (EWA) surface splatting with time‐varying scenes. We extend the theoretical basis of the original framework by replacing the 2D surface reconstruction filters by 3D kernels which unify the spatial and temporal component of moving objects. Based on the newly derived mathematical framework we introduce a rendering algorithm that supports the generation of high‐quality motion blur for point‐based objects using a piecewise linear approximation of the motion. The rendering algorithm applies ellipsoids as rendering primitives which are constructed by extending planar EWA surface splats into the temporal dimension along the instantaneous motion vector. Finally, we present an implementation of the proposed rendering algorithm with approximated occlusion handling using advanced features of modern GPUs and show its capability of producing motion‐blurred result images at interactive frame rates.

Abstract Animations of hair dynamics greatly enrich the visual attractiveness of human characters. Traditional simulation techniques handle hair as clumps or continuum for efficiency; however, the visual quality is limited because they cannot represent the fine‐scale motion of individual hair strands. Although a recent mass‐spring approach tackled the problem of simulating the dynamics of every strand of hair, it required a complicated setting of springs and suffered from high computational cost. In this paper, we base the animation of hair on such a fine‐scale on Lattice Shape Matching (LSM), which has been successfully used for simulating deformable objects. Our method regards each strand of hair as a chain of particles, and computes geometrically derived forces for the chain based on shape matching. Each chain of particles is simulated as an individual strand of hair. Our method can easily handle complex hairstyles such as curly or afro styles in a numerically stable way. While our method is not physically based, our GPU‐based simulator achieves visually plausible animations consisting of several tens of thousands of hair strands at interactive rates.

Abstract This paper presents a fast method that computes deformations with fracturing of an object using a hierarchical lattice. Our method allows numerically stable computation based on so‐called shape matching. During the simulation, the deformed shape of the object and the condition of fracturing are used to determine the appropriate detail level in the hierarchy of the lattices. Our method modifies the computation of the stiffness of the object in different levels of the hierarchy so that the stiffness is maintained uniform by introducing a stiffness parameter that does not depend on the hierarchy. By merging the subdivided lattices, our method minimizes the increase of computational cost. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Metaballs are implicit surfaces widely used to model curved objects, represented by the isosurface of a density field defined by a set of points. Recently, the results of particle‐based simulations have been often visualized using a large number of metaballs, however, such visualizations have high rendering costs. In this paper we propose a fast technique for rendering metaballs on the GPU. Instead of using polygonization, the isosurface is directly evaluated in a per‐pixel manner. For such evaluation, all metaballs contributing to the isosurface need to be extracted along each viewing ray, on the limited memory of GPUs. We handle this by keeping a list of metaballs contributing to the isosurface and efficiently update it. Our method neither requires expensive precomputation nor acceleration data structures often used in existing ray tracing techniques. With several optimizations, we can display a large number of moving metaballs quickly.

Abstract Recent advances in physically‐based simulations have made it possible to generate realistic animations. However, in the case of solid‐fluid coupling, wetting effects have rarely been noticed despite their visual importance especially in interactions between fluids and granular materials. This paper presents a simple particle‐based method to model the physical mechanism of wetness propagating through granular materials; Fluid particles are absorbed in the spaces between the granular particles and these wetted granular particles then stick together due toliquid bridgesthat are caused by surface tension and which will subsequently disappear when over‐wetting occurs. Our method can handle these phenomena by introducing awetness valuefor each granular particle and by integrating those aspects of behavior that are dependent on wetness into the simulation framework. Using this method, a GPU‐based simulator can achieve highly dynamic animations that include wetting effects in real time.