We introduce the task of Audible Action Temporal Localization, which aims to identify the spatio-temporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose $TA^{2}Net$, a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio input. $TA^{2}Net$ also integrates a self-supervised spatial localization strategy during training, combining contrastive learning with spatial analysis. This dual design improves temporal localization accuracy and simultaneously identifies sound sources within video frames. To support this task, we introduce a new benchmark dataset, $Audible623$, derived from Kinetics and UCF101 by removing non-essential vocalization subsets. Extensive experiments confirm the effectiveness of our approach on $Audible623$ and show strong generalizability to other domains, such as repetitive counting and sound source localization. Code and dataset are available at https://github.com/WenlongWan/Audible623.

Recently, single-image SVBRDF capture is formulated as a regression problem, which uses a network to infer four SVBRDF maps from a flash-lit image. However, the accuracy is still not satisfactory since previous approaches usually adopt end-to-end inference strategies. To mitigate the challenge, we propose "auxiliary renderings" as the intermediate regression targets, through which we divide the original end-to-end regression task into several easier sub-tasks, thus achieving better inference accuracy. Our contributions are threefold. First, we design three (or two pairs of) auxiliary renderings and summarize the motivations behind the designs. By our design, the auxiliary images are bumpiness-flattened or highlight-removed, containing disentangled visual cues about the final SVBRDF maps and can be easily transformed to the final maps. Second, to help estimate the auxiliary targets from the input image, we propose two mask images including a bumpiness mask and a highlight mask. Our method thus first infers mask images, then with the help of the mask images infers auxiliary renderings, and finally transforms the auxiliary images to SVBRDF maps. Third, we propose backbone UNets to infer mask images, and gated deformable UNets for estimating auxiliary targets. Thanks to the well-designed networks and intermediate images, our method outputs better SVBRDF maps than previous approaches, validated by the extensive comparisonal and ablation experiments.

We introduce the Self-Exemplar Illumination Equalization Network, designed specifically for effective portrait shadow removal. The core idea of our method is that partially shadowed portraits can find ideal exemplars within their non-shadowed facial regions. Rather than directly fusing two distinct classes of facial features, our approach utilizes non-shadowed regions as an illumination indicator to equalize the shadowed regions, generating deshadowed results without boundary-merging artifacts. Our network comprises cascaded Self-Exemplar Illumination Equalization Blocks (SExmBlock), each containing two modules: a self-exemplar feature matching module and a feature-level illumination rectification module. The former identifies and applies internal illumination exemplars to shadowed areas, producing illumination-corrected features, while the latter adjusts shadow illumination by reapplying the illumination factors from these features to the input face. Applying this series of SExmBlocks to shadowed portraits incrementally eliminates shadows and preserves clear, accurate facial details. The effectiveness of our method is demonstrated through evaluations on two public shadow portrait datasets, where it surpasses existing state-of-the-art methods in both qualitative and quantitative assessments.

In this study, we present a new and innovative framework for acquiring high-quality SVBRDF maps. Our approach addresses the limitations of the current methods and proposes a new solution. The core of our method is a simple hardware setup consisting of a consumer-level camera, LED lights, and a carefully designed network that can accurately obtain the high-quality SVBRDF properties of a nearly planar object. By capturing a flexible number of images of an object, our network uses different subnetworks to train different property maps and employs appropriate loss functions for each of them. To further enhance the quality of the maps, we improved the network structure by adding a novel skip connection that connects the encoder and decoder with global features. Through extensive experimentation using both synthetic and real-world materials, our results demonstrate that our method outperforms previous methods and produces superior results. Furthermore, our proposed setup can also be used to acquire physically based rendering maps of special materials.

Constructing a convex hull for the pixel colors of an image by viewing them as 3D points can extract a set of palette colors for the image, then image recoloring can be achieved by modifying the palette colors. For better recoloring effect, the convex hull should contain more pixels (inclusive) and be more compact. Otherwise, reconstruction error would occur or the extracted palette color would be less representative, yielding wrong recoloring results or less effective edit. We observe that convex hulls constructed by prior methods can contain all the image pixels, but are far from compact. Efforts have been made to optimize the vertices of convex hull to increase the compactness but are still not perfect. In this paper, we propose a novel coarse to fine convex hull construction scheme with auxiliary vertices. We start by constructing a coarse convex hull whose vertices are directly image pixels which is thus the most compact but cannot contain all pixels. We then make a remedy by adding auxiliary vertices into the coarse convex hull to obtain a fine convex hull. More auxiliary vertices are added, more image pixels will be contained into the fine convex hull. The auxiliary vertices are image pixels too so that the compactness can still be maintained. During editing, the auxiliary vertices are not allowed to be edited for edit convenience, but deformed as-rigid-as-possible with the adjusting of other vertices. Our convex hull is both inclusive and compact. Extensive experiments validate the effectiveness of the proposed method.

Enhancing low-light images is challenging as it requires simultaneously handling global and local contents. This paper presents a new solution which incorporates the vision transformer (ViT) into Laplacian pyramid and explores cross-layer dependence within the pyramid. It first applies Laplacian pyramid to decompose the low-light image into a low-frequency (LF) component and several high-frequency (HF) components. As the LF component has a low resolution and mainly includes global attributes, ViT is applied on it to explore the interdependence among global contents. Since there exists strong spatial correlation among different frequency components, the refined features from a lower pyramid layer are used to assist the refinement of upper-layer features. Experiments demonstrate that our approach achieves better performance than state-of-the-art methods, while maintaining a relative small model size and low computational complexity. Our source code and trained model will be released at https://github.com/Xinjie-Wei/DLEN.

Color pencil drawing is well-loved due to its rich expressiveness. This paper proposes an approach for generating feature-preserving color pencil drawings from photographs. To mimic the tonal style of color pencil drawings, which are much lighter and have relatively lower saturation than photographs, we devise a lightness enhancement mapping and a saturation reduction mapping. The lightness mapping is a monotonically decreasing derivative function, which not only increases lightness but also preserves input photograph features. Color saturation is usually related to lightness, so we suppress the saturation dependent on lightness to yield a harmonious tone. Finally, two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are consistent. Comprehensive experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

Abstract Human non‐speech sounds occur during expressions in a real‐life environment. Realising a person's incapability to prompt confident expressions by non‐speech sounds may assist in identifying premature disorder in medical applications. A novel dataset named Nonspeech7k is introduced that contains a diverse set of human non‐speech sounds, such as the sounds of breathing, coughing, crying, laughing, screaming, sneezing, and yawning. The authors then conduct a variety of classification experiments with end‐to‐end deep convolutional neural networks (CNN) to show the performance of the dataset. First, a set of typical deep classifiers are used to verify the reliability and validity of Nonspeech7k. Involved CNN models include 1D‐2D deep CNN EnvNet, deep stack CNN M11, deep stack CNN M18, intense residual block CNN ResNet34, modified M11 named M12, and the authors’ baseline model. Among these, M12 achieves the highest accuracy of 79%. Second, to verify the heterogeneity of Nonspeech7k with respect to two typical datasets, FSD50K and VocalSound, the authors design a series of experiments to analyse the classification performance of deep neural network classifier M12 by using FSD50K, FSD50K + Nonspeech7k, VocalSound, VocalSound + Nonspeech7k as training data, respectively. Experimental results show that the classifier trained with existing datasets mixed with Nonspeech7k achieves the highest accuracy improvement of 15.7% compared to that without Nonspeech7k mixed. Nonspeech7k is 100% annotated, completely checked, and free of noise. It is available at https://doi.org/10.5281/zenodo.6967442 .

Abstract For 3D mesh pose transfer, the target model is obtained by transferring the pose of the reference mesh to the source mesh, where the shape and pose of the source are usually different from that of the reference. In this paper, pose transfer is considered as a deformation process of the source mesh, and we propose a 3D mesh pose transfer method based on skeletal deformation. First, we design a neural network based on the edge convolution operator to extract the skeleton of the 3D mesh and bind the rigid weights; then, we calculate the bone transformations between the two skeletons with different poses and use the diffusion equation to smooth the rigid weights; finally, the source mesh is deformed according to the bone transformations and the smooth weights to get the target mesh. Experiment results on different datasets show that the pose of the reference mesh can be effectively transferred to the source one while maintaining the shape and high‐quality geometric details of the source mesh by using our method.

Simulating liquid-textile interaction has received great attention in computer graphics recently. Most existing methods take textiles as particles or parameterized meshes. Although these methods can generate visually pleasing results, they cannot simulate water content at a microscopic level due to the lack of geometrically modeling of textile's anisotropic structure. In this paper, we develop a method for yarn-level simulation of hygroscopicity of textiles and evaluate it using various quantitative metrics. We model textiles in a fiber-yarn-fabric multi-scale manner and consider the dynamic coupled physical mechanisms of liquid spreading, including wetting, wicking, moisture sorption/desorption, and transient moisture-heat transfer in textiles. Our method can accurately simulate liquid spreading on textiles with different fiber materials and geometrical structures with consideration of air temperatures and humidity conditions. It visualizes the hygroscopicity of textiles to demonstrate their moisture management ability. We conduct qualitative and quantitative experiments to validate our method and explore various factors to analyze their influence on liquid spreading and hygroscopicity of textiles.

This paper presents a high-quality human motion pre-diction method that accurately predicts future human poses given observed ones. Our method is based on the observation that a good “initial guess” of the future poses is very helpful in improving the forecasting accuracy. This mo-tivates us to propose a novel two-stage prediction frame-work, including an init-prediction network that just computes the good guess and then a formal-prediction network that predicts the target future poses based on the guess. More importantly, we extend this idea further and design a multi-stage prediction framework where each stage pre-dicts initial guess for the next stage, which brings more performance gain. To fulfill the prediction task at each stage, we propose a network comprising Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph Convolutional Networks (T-DGCN). Alternatively executing the two networks helps extract spatiotem-poral features over the global receptive field of the whole pose sequence. All the above design choices cooperating together make our method outperform previous approaches by large margins: 6%-7% on Human3.6M, 5%-10% on CMU-MoCap, and 13%-16% on 3DPW. Code is available at https://github.com/705062791/PGBIG.

Diverse human motion prediction aims at predicting multiple possible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sample outcomes from the distribution. While different results can be obtained, they are usually the most likely ones which are not diverse enough. Recent work explicitly learns multiple modes of the conditional distribution via a deterministic network, which however can only cover a fixed number of modes within a limited range. In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model. Our method works by generating an auxiliary space and smartly making randomly sampling from the auxiliary space equivalent to the diverse sampling from the target distribution. We propose a simple yet effective network architecture that implements this novel sampling strategy, which incorporates a Gumbel-Softmax coefficient matrix sampling method and an aggressive diversity promoting hinge loss function. Extensive experiments demonstrate that our method significantly improves both the diversity and accuracy of the samplings compared with previous state-of-the-art sampling approaches. Code and pre-trained models are available at https://github.com/Droliven/diverse_sampling.

Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursively to obtain a set of poses at multiple scales. With the increase of the abstraction level, the motion of the pose becomes more stable, which benefits pose prediction too. In this paper, we propose a novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end. The GCNs are used to extract features from fine to coarse scale and then from coarse to fine scale. The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses. Intermediate supervisions are imposed on all the predicted poses, which enforces the network to learn more representative features. Our proposed approach is evaluated on two standard benchmark datasets, i.e., the Human3.6M dataset and the CMU Mocap dataset. Experimental results demonstrate that our method outperforms the state-of-the-art approaches. Code and pre-trained models are available at https://github.com/Droliven/MSRGCN.

In this paper, we propose HF2-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between video frame and optical flow, to predict the next frame given several previous frames. By CVAE, the quality of flow reconstruction essentially influences that of frame prediction. Therefore, poorly reconstructed optical flows of abnormal events further deteriorate the quality of the final predicted future frame, making the anomalies more detectable. Experimental results demonstrate the effectiveness of the proposed method. Code is available at https://github.com/LiUzHiAn/hf2vad.

Parameterizing Variations of human shapes and motions is a long-standing problem in computer graphics and vision. Most of the existing methods only deal with a specific kind of motion, such as body poses, facial expressions, or hand gestures. We propose PanoMan (sParse locAlized compoNents based mOdel for full huMAn motioNs) to handle shape variation and full-motion across body, face, and hand in a unified framework. Like previous approaches, we factor shape variation into principal components to obtain a human shape space that approximates the shape of arbitrary identity. We then analyze sparse localized components in terms of relative edge length and dihedral angle to capture full motions of body poses, facial expressions, and hand gestures. The final piece of our model is a multilayer perceptron (MLP) that fits the residual between the ground truth and the aforementioned two-level approximation. As an application, we employ the discrete-shell deformation to drive the model to fit sparse constraints such as joint positions and surface feature points. We thoroughly evaluate PanoMan on body, face, and hand motion benchmarks as well as scanned data. The existing skinning-based techniques suffer from joint collapsing when encountering twisting motion of joints. Experiments show that PanoMan can capture all kinds of full human motions with high quality and is easier than the state-of-the-art models in recovering poses with wide joint twisting and complex hand gestures.

While self-attention has been successfully applied in a variety of natural language processing and computer vision tasks, its application in Monte Carlo (MC) image denoising has not yet been well explored. This paper presents a self-attention based MC denoising deep learning network based on the fact that self-attention is essentially non-local means filtering in the embedding space which makes it inherently very suitable for the denoising task. Particularly, we modify the standard self-attention mechanism to an auxiliary feature guided self-attention that considers the by-products (e.g., auxiliary feature buffers) of the MC rendering process. As a critical prerequisite to fully exploit the performance of self-attention, we design a multi-scale feature extraction stage, which provides a rich set of raw features for the later self-attention module. As self-attention poses a high computational complexity, we describe several ways that accelerate it. Ablation experiments validate the necessity and effectiveness of the above design choices. Comparison experiments show that the proposed self-attention based MC denoising method outperforms the current state-of-the-art methods.

Abstract Hair modeling plays an important role in computer animation, virtual reality, and other applications. This paper proposes an encoder‐decoder network, named HAO‐CNN, to recover 3D hair strand models from a single image. Specifically, HAO‐CNN generates a volumetric vector field (VVF) from the oriented map of hairstyles. However, instead of directly working on the full resolution VVFs, we introduce the adapted O‐CNN to predict the adaptive representation of VVFs in order to greatly reduce the memory cost. In addition, we fuse the features from different layers of the encoding stage for both capturing the global structure and being aware of hair filaments. Considering the difficulty of acquiring true three‐dimensional (3D) hair models, we augment the dataset with 340 3D hair models by 1,800 hair models via interactive editing using the software and render their oriented maps as training data. Then given a hair photo associated with human head, we segment out the hair region, compute its two‐dimensional oriented map using Gabor filter, and feed it into the network to produce a hair volumetric vector field which is then converted into hairline models using an improved VVF‐to‐strands algorithm. This greatly decreases the time cost of approaches based on volumetric vector fields.

We propose an interactive contour extraction method inspired by a skill often adopted in sketching: an artist usually sketches an object by first drawing lots of short, directional, and redundant strokes, then following these small strokes to draw the final outline of the object. Our method simulates this process. To extract a contour, our method relies on user interaction, which provides us with a narrow band containing the target contour. Then, we densely sample sub-bands from the whole band, with each sub-band containing a local segment of the target contour. We design a curve-centered coordinate system in which a dynamic programming algorithm is proposed to extract the local segment in each sub-band. The local segment is guaranteed to be as evident and smooth as possible, to mimic the strokes sketched by the artist. Finally, we integrate all local segments of all sub-bands together to obtain the whole target contour based on the weighted principal component analysis. Our method can extract high-quality object contours due to the dense validations among local segments. That is, even if one segment deviates from the right location, several other segments in its local neighborhood can correct it in the integration stage. Both quantitative experiments and a user study demonstrate the effectiveness of the proposed method.

We present a method for synopsizing multiple videos captured by a set of surveillance cameras with some overlapped field-of-views. Currently, object-based approaches that directly shift objects along the time axis are already able to compute compact synopsis results for multiple surveillance videos. The challenge is how to present the multiple synopsis results in a more compact and understandable way. Previous approaches show them side by side on the screen, which however is difficult for user to comprehend. In this paper, we solve the problem by joint object-shifting and camera view-switching. Firstly, we synchronize the input videos, and group the same object in different videos together. Then we shift the groups of objects along the time axis to obtain multiple synopsis videos. Instead of showing them simultaneously, we just show one of them at each time, and allow to switch among the views of different synopsis videos. In this view switching way, we obtain just a single synopsis results consisting of content from all the input videos, which is much easier for user to follow and understand. To obtain the best synopsis result, we construct a simultaneous object-shifting and view-switching optimization framework instead of solving them separately. We also present an alternative optimization strategy composed of graph cuts and dynamic programming to solve the unified optimization. Experiments demonstrate that our single synopsis video generated from multiple input videos is compact, complete, and easy to understand.

Video stabilization is usually composed of three stages: feature trajectory extraction, trajectory smoothing, and frame warping. Most previous approaches view them as three separate stages. This paper proposes a method combining the last two stages, namely the trajectory smoothing and frame warping stages, into a single optimization framework. The novelty exists in the way of how we combine them: the trajectory smoothing part plays a major role while the frame warping part plays an auxiliary role. With this kind of design, we can conveniently increase the strength of the trajectory smoothing part by a robust first-order derivative term, which makes it possible to produce very aggressive stabilization effects. On the other hand, we adopt adaptive weighting mechanisms in the frame warping part, to follow the smoothed trajectories as much as possible while regularizing other places as similar as possible. Our method is robust to utilize both foreground and background features, and very short trajectories. The utilization of all these information in turn increases the accuracy of the proposed method. We also provide a simplified implementation of our method, which is less accurate but more efficient. Experiments on various kinds of videos demonstrate the effectiveness of our method.

Stitching videos captured by hand-held mobile cameras can essentially enhance entertainment experience of ordinary users. However, such videos usually contain heavy shakiness and large parallax, which are challenging to stitch. In this paper, we propose a novel approach of video stitching and stabilization for videos captured by mobile devices. The main component of our method is a unified video stitching and stabilization optimization that computes stitching and stabilization simultaneously rather than does each one individually. In this way, we can obtain the best stitching and stabilization results relative to each other without any bias to one of them. To make the optimization robust, we propose a method to identify background of input videos, and also common background of them. This allows us to apply our optimization on background regions only, which is the key to handle large parallax problem. Since stitching relies on feature matches between input videos, and there inevitably exist false matches, we thus propose a method to distinguish between right and false matches, and encapsulate the false match elimination scheme and our optimization into a loop, to prevent the optimization from being affected by bad feature matches. We test the proposed approach on videos that are causally captured by smartphones when walking along busy streets, and use stitching and stability scores to evaluate the produced panoramic videos quantitatively. Experiments on a diverse of examples show that our results are much better than (challenging cases) or at least on par with (simple cases) the results of previous approaches.Stitching videos captured by hand-held mobile cameras can essentially enhance entertainment experience of ordinary users. However, such videos usually contain heavy shakiness and large parallax, which are challenging to stitch. In this paper, we propose a novel approach of video stitching and stabilization for videos captured by mobile devices. The main component of our method is a unified video stitching and stabilization optimization that computes stitching and stabilization simultaneously rather than does each one individually. In this way, we can obtain the best stitching and stabilization results relative to each other without any bias to one of them. To make the optimization robust, we propose a method to identify background of input videos, and also common background of them. This allows us to apply our optimization on background regions only, which is the key to handle large parallax problem. Since stitching relies on feature matches between input videos, and there inevitably exist false matches, we thus propose a method to distinguish between right and false matches, and encapsulate the false match elimination scheme and our optimization into a loop, to prevent the optimization from being affected by bad feature matches. We test the proposed approach on videos that are causally captured by smartphones when walking along busy streets, and use stitching and stability scores to evaluate the produced panoramic videos quantitatively. Experiments on a diverse of examples show that our results are much better than (challenging cases) or at least on par with (simple cases) the results of previous approaches.

In[1], the biographies of Hanqiu Sun and Guiqing Li included incorrect information. The correct biographies are as follows.

Abstract Compactly representing time‐varying geometries is an important issue in dynamic geometry processing. This paper proposes a framework of sparse localized decomposition for given animated meshes by analyzing the variation of edge lengths and dihedral angles (LAs) of the meshes. It first computes the length and dihedral angle of each edge for poses and then evaluates the difference (residuals) between the LAs of an arbitrary pose and their counterparts in a reference one. Performing sparse localized decomposition on the residuals yields a set of components which can perfectly capture local motion of articulations. It supports intuitive articulation motion editing through manipulating the blending coefficients of these components. To robustly reconstruct poses from altered LAs, we devise a connection‐map‐based algorithm which consists of two steps of linear optimization. A variety of experiments show that our decomposition is truly localized with respect to rotational motions and outperforms state‐of‐the‐art approaches in precisely capturing local articulated motion.

Abstract This paper presents a new two‐step color transfer method which includes color mapping and detail preservation. To map source colors to target colors, which are from an image or palette, the proposed similarity‐preserving color mapping algorithm uses the similarities between pixel color and dominant colors as existing algorithms and emphasizes the similarities between source image pixel colors. Detail preservation is performed by an ℒ0 gradient‐preserving algorithm. It relaxes the large gradients of the sparse pixels along color region boundaries and preserves the small gradients of pixels within color regions. The proposed method preserves source image color similarity and image details well. Extensive experiments demonstrate that the proposed approach has achieved a state‐of‐art visual performance.

Wide-baseline street image interpolation is useful but very challenging. Existing approaches either rely on heavyweight 3D reconstruction or computationally intensive deep networks. We present a lightweight and efficient method which uses simple homography computing and refining operators to estimate piecewise smooth homographies between input views. To achieve the goal, we show how to combine homography fitting and homography propagation together based on reliable and unreliable superpixel discrimination. Such a combination, other than using homography fitting only, dramatically increases the accuracy and robustness of the estimated homographies. Then, we integrate the concepts of homography and mesh warping, and propose a novel homography-constrained warping formulation which enforces smoothness between neighboring homographies by utilizing the first-order continuity of the warped mesh. This further eliminates small artifacts of overlapping, stretching, etc. The proposed method is lightweight and flexible, allows wide-baseline interpolation. It improves the state of the art and demonstrates that homography computation suffices for interpolation. Experiments on city and rural datasets validate the efficiency and effectiveness of our method.

Abstract Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter‐frame variation across the whole sequence. We propose alocal‐to‐globalapproach toco‐segmentpoint cloud sequences of articulated objects into near‐rigid moving parts. Our method starts from a per‐frame point clustering, derived from a robust voting‐based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novelspace‐time segment groupingtechnqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co‐segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data.

Abstract Rig‐space physics a finite element method(FEM) based simulation technique that aims at adding secondary motion on a character while maintaining seamless cooperation with traditional animation pipelines. We enhance the rig‐space physics by introducing several techniques, including general field interaction, proportional‐derivative control, and improved material control. This allows an animator to perform various interferences to the simulation process and create more abundant animation effects. Moreover, we also improve the numerical stability of the simulation algorithm by prepending a conjugate gradient procedure. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Cage‐based deformation has become a popular method for shape deformation in computer graphics and animation. To edit a shape first requires a cage to be built to envelop the target model which is a tedious work by manual approaches. In this paper, we develop an automatic method to generate the cage for a model using voxelization based decomposition. We first voxelize the input model, and then use the seed filling algorithm to group the inner voxels. By dilating the inner voxel groups, we decompose the model into broad regions and narrow regions. Then we construct partial cages using different strategies and unite them to get a cage. Experiment results demonstrate that our method is effective, efficient as well as robust to model transformation. Copyright © 2014 John Wiley & Sons, Ltd.

This paper proposes a framework for matting identification photos automatically. We firstly adopt image processing operations such as skin detection, k-means clustering, Grab cut segmentation and canny operator to generate a trimap with three regions, i.e., background, foreground and unknown regions. Then, we improve the Bayesian matting by introducing an alpha regularization term. Experiments demonstrate that our system can achieve about 86% acceptance rate for real photo datasets.

We introduce L 1 - medial skeleton as a curve skeleton representation for 3D point cloud data. The L 1 -median is well-known as a robust global center of an arbitrary set of points. We make the key observation that adapting L 1 -medians locally to a point set representing a 3D shape gives rise to a one-dimensional structure, which can be seen as a localized center of the shape. The primary advantage of our approach is that it does not place strong requirements on the quality of the input point cloud nor on the geometry or topology of the captured shape. We develop a L 1 -medial skeleton construction algorithm, which can be directly applied to an unoriented raw point scan with significant noise, outliers, and large areas of missing data. We demonstrate L 1 -medial skeletons extracted from raw scans of a variety of shapes, including those modeling high-genus 3D objects, plant-like structures, and curve networks.

The appearance of woven fabrics is intrinsically determined by the geometric details of their meso/micro scale structure. In this paper, we propose a multiscale representation and tessellation approach for woven fabrics. We extend the Displaced Subdivision Surface (DSS) to a representation named Interlaced/Intertwisted Displacement Subdivision Surface (IDSS). IDSS maps the geometric detail, scale by scale, onto a ternary interpolatory subdivision surface that is approximated by Bezier patches. This approach is designed for woven fabric rendering on DX11 GPUs. We introduce the Woven Patch, a structure based on DirectX’s new primitive, patch, to describe an area of a woven fabric so that it can be easily implemented in the graphics pipeline using a hull shader, a tessellator and a domain shader. We can render a woven piece of fabric at 25 frames per second on a low-performance NVIDIA 8400 MG mobile GPU. This allows for large-scale representations of woven fabrics that maintain the geometric variances of real yarn and fiber.

This paper investigates the convergence of an algorithm geometrically interpolating a given polygon using uniform quadratic and cubic B-splines respectively. The geometric interpolation method views the polygon itself as the initial guess of the control polygon of the B-spline and reduces the approximate error by iteratively updating the control points with the deviation from the interpolated vertices to their nearest foot points on the current B-spline curve. We demonstrate that the algorithm usually does not converge if the nearest points are searched on the whole curve and present a sufficient condition under which the algorithm is convergent, for quadratic and cubic B-splines respectively. Furthermore, we introduce a new strategy to update the control points incrementally. Experiments show that though our condition constrains the search range of the nearest points, it can still produce interpolation curves with the same high quality as the original method.

This paper describes an approach to the approximation of Loop subdivision surfaces for real-time rendering. The approach consists of two phases, which separately construct the approximation geometry and the normal field of a subdivision surface. It first exploits quartic triangular Bézier patches to approximate the geometry of the subdivision surface by interpolating a grid of sampled points. To remedy the artifact of discontinuity of normal fields between adjacent patches, a continuous normal field is then reconstructed by approximating the tangent vector fields of the subdivision surfaces with quartic triangular Bézier patches. For regular triangles, the approach reproduces the associated subdivision patches, quartic three-directional box splines.

Generation of a large-scale city requires a significant amount of manual work and computation to process massive location information and model building geometry with multi-level of details. Normally, an urban city is heavily built-up with different architectural building patterns across extensively and topographically varied landscapes. In this paper, we introduce Footprint-Profile Sweep Surfaces (FPSS), a flexible and computationally efficient approach for realtime generation and rendering of massive urban buildings in a heavily built-up city. A solid constituting an urban building is represented as an instance of FPSS and is generated by sweeping a footprint along a profile with specific parameters. We present two forms of FPSS: super FPSS to address the shapes from architecture design and poly FPSS to address the shapes from imported GIS data. We make use of hardware tessellation to allow dynamic LOD according to view distance. A special scaling-translation-rotation displacement performed on the simplified profile is proposed to support detail generation. Experimental results show that realtime performance can be achieved using our approach to generate varied styles of urban buildings. Even inexperienced users are able to generate a building group quickly in their own style based on FPSS.

This paper applies a modified composite radic(2) subdivision framework to an extensive family of box splines, and therefore generalizes these box splines to irregular control meshes. Particularly, a variant of the quad subdivision of Peters and Shiue (2004) is shown as a special case of the new framework. In addition, a new dual subdivision scheme is also derived, as a generalization of some special box splines. Towards the practical use, the unified framework is also extended for modelling boundary and crease features.

Abstract This paper presents a universal method for constructing interpolatory subdivision schemes from known approximatory subdivisions. The method establishes geometric rules of the associated interpolatory subdivision through addition of further weighted averaging operations to the approximatory subdivision. The paper thus provides a novel approach for designing new interpolatory subdivision schemes. In addition, a family of subdivision surfaces varying from the given approximatory scheme to its associated interpolatory scheme, namely the blending subdivisions, can also be established. Based on the proposed method, variants of several known interpolatory subdivision schemes are constructed. A new interpolatory subdivision scheme is also developed using the same technique. Brief analysis of a family of blending subdivisions associated with the Loop subdivision scheme demonstrates that this particular family of subdivisions are globally C1 continuous while maintaining bounded curvature for regular meshes. As a further extension of the blending subdivisions, a volume‐preserving subdivision strategy is also proposed in the paper.

Abstract This paper presents a new interpolatory subdivision scheme for quadrilateral meshes based on a 1–4 splitting operator. The scheme generates surfaces coincident with those of the Kobbelt interpolatory subdivision scheme for regular meshes. A new group of rules are designed for computing newly inserted vertices around extraordinary vertices. As an extension of the regular masks,the new rules are derived based on a reinterpretation of the regular masks. Eigen‐structure analysis demonstrates that subdivision surfaces generated using the new scheme are C1continuous and, in addition, have bounded curvature.

This paper presents a new interpolatory subdivision for quadrilateral meshes. The proposed scheme employs a /spl radic/2 split operator to refine a given control mesh such that the face number of the refined mesh is doubled after each refinement. For regular meshes, the smallest mask is chosen to calculate newly inserted vertices and special rules are developed to compute the F-vertices for irregular faces based on the Fourier analysis of block circulant matrices. Numerical analysis manifests that the scheme yields globally C1 continuous limit surfaces. Finally, an extension to arbitrary polygonal meshes is considered.

A representation, the generalized NURBS (G-NURBS), is proposed for modeling parametric curves and surfaces. G-NURBS provides a unified framework for traditional parametric curve and surface. G-NURBS surface based on arbitrary irregular mesh can represent closed surface or trimmed surface with only one surface patch.

A clothoid spline is a planar G/sup 2/ curve with piecewise linear curvature. Its discrete analogon, planar discrete clothoid spline (PDCS for short) generated by non-linear subdivision, is also of high quality owing to piecewise linear curvature distribution. We extend the PDCS to 3D by introducing discrete curvature binormal vectors and a discrete Frenet frame. An algorithm similar to the planar case is developed for creation of 3D discrete clothoid splines, Experiments show that 3D discrete clothoid spline still retains high quality. This result makes it possible to construct discrete clothoid spline surfaces on open triangle meshes or curve nets of arbitrary topology.