Accurate and timely identification of key gestures is fundamental to many virtual reality and metaverse applications. Our goal is to address the demand for expertise in areas such as deep learning that developers are confronted with when integrating key gesture spotting into their VR applications. Previous methods relied on multiple sensors or camera arrays to collect gesture signals, which lead to uncomfortable user experience. However, simply removing sensors often results in performance collapse. In this paper, we propose a novel few-shot framework for key gesture spotting that can be integrated into VR applications, allowing developers and users to create their own key gestures as triggers to perform specific behaviours. Our method involves using high-resolution gesture sequences as sample inputs during training, encoding and storing them as latent representations. During evaluation, low-resolution sequences are input and matched for similarity with the representations in the memory bank. We then compute weights and perform a weighted sum of both, aiming to provide additional information for the low-resolution inputs. Experiments show that enhancing the cosine similarity classifier with meta-learning and a memory bank achieves near state-of-the-art accuracy efficiently, with minimal overhead, and can be achieved using only a head-mounted VR device.

With the progress of computer vision and machine learning, the research of object detection and pedestrian recognition has demonstrated significant performance. However, the identification studies in domestic animals, especially in the same species of domestic animals, remains a significant challenge. His study focuses on distinguishing cashmere and dairy goats, which share similar traits. Our contributions are: (1) Proposing a dynamic quadruple optimization algorithm to optimize goat images from local and global dimensions, enhancing network representation with a multi-branch structure; (2) Introducing a novel transfer learning algorithm based on goat granularity to preview dataset knowledge; (3) Validating our approach on our goat dataset and a public bird dataset. We achieved recognition accuracies of 95% for cashmere goats, 94.04% for dairy goats, and 82.48% on the public dataset, demonstrating the effectiveness of our methods for animal biometric identification.

ABSTRACT The underground coal mine working environment is complicated, and the detection of safety helmet wearing is vital for assuring worker safety. This article proposes an improved YOLOv8n safety helmet detection model, YOLOv8‐HAC, to address the issues of coexisting strong light exposure and low illumination, equipment occlusions that result in partial target loss, and the missed detection of small targets due to limited surveillance perspectives in underground coal mines. The model substitutes the suggested HAC‐Net for the C2f module in YOLOv8n's backbone network to improve feature extraction and detection performance for targets with motion blur and low‐resolution images. To improve detection stability in complicated situations and lessen background interference, the AGC‐Block module is also included for dynamic feature selection. Additionally, a tiny target detection layer is included to increase the long‐range identification rate of tiny safety helmets. According to experimental data, the enhanced model outperforms existing popular object detection algorithms, with a mAP of 94.8% and a recall rate of 90.4%. This demonstrates how well the suggested approach works to identify safety helmets in situations with complicated lighting and low‐resolution photos.

ABSTRACT Chinese painting is the traditional way of painting in China, with distinctive artistic characteristics and a strong national style. Creating Chinese paintings is a complex and difficult process for non‐experts, so utilizing computer‐aided Chinese painting generation is a meaningful topic. In this paper, we propose a novel Chinese painting generation model, which can generate vivid Chinese paintings in a stroke‐by‐stroke manner. In contrast to previous neural renderers, we design a Chinese painting renderer that can generate two classic stroke types of Chinese painting (i.e., middle‐tip stroke and side‐tip stroke), without the aid of any neural network. To capture the subtle semantic representation from the input image, we design a semantic loss to compute the distance between the input image and the output Chinese painting. Experiments demonstrate that our method can generate vivid and elegant Chinese paintings.

ABSTRACT Accurate 3D interacting hand mesh reconstruction from RGB images is crucial for applications such as robotics, augmented reality (AR), and virtual reality (VR). Especially in the field of robotics, accurate interacting hand mesh reconstruction can significantly improve the accuracy and naturalness of human‐robot interaction. This task requires an accurate understanding of complex interactions between two hands and ensuring reasonable alignment of the hand mesh with the image. Recent Transformer‐based methods directly utilize the features of the two hands as input tokens, ignoring the correlation between local and global features of the interacting hands, leading to hand ambiguity, self‐occlusion, and self‐similarity problems. We propose LGNet, Local and Global Feature Adaptive Network, through separating the hand mesh reconstruction process into three stages: A joint stage for predicting hand joints; a mesh stage for predicting a rough hand mesh; and a refine stage for fine‐tuning the mesh‐image alignment using an offset mesh. LGNet enables high‐quality fingertip‐level mesh‐image alignment, effectively models the spatial relationship between two hands, and supports real‐time prediction. Comprehensive quantitative and qualitative evaluations on benchmark datasets reveal that LGNet surpasses existing methods in mesh accuracy and alignment accuracy, while also showcasing robust generalization performance in tests on in‐the‐wild images.

Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, methods based upon masking Autoencoders have been explored in the fields. The input can be intuitively masked due to regular content, like sequence words and 2D pixels. However, the extension to 3D point cloud is challenging due to irregularity. In this paper, we propose masked Autoencoders in 3D point cloud representation learning (abbreviated as MAE3D), a novel autoencoding paradigm for self-supervised learning. We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches. Secondly, we employ patch-wise MAE3D Transformers to learn both local features of point cloud patches and high-level contextual relationships between patches, then complete the latent representations of masked patches. We use our Point Cloud Reconstruction Module with multi-task loss to complete the incomplete point cloud as a result. We conduct self-supervised pre-training on ShapeNet55 with the point cloud completion pre-text task and fine-tune the pre-trained model on ModelNet40 and ScanObjectNN (PB_T50_RS, the hardest variant). Comprehensive experiments demonstrate that the local features extracted by our MAE3D from point cloud patches are beneficial for downstream classification tasks, soundly outperforming state-of-the-art methods (93.4% and 86.2% classification accuracy, respectively).Our source codes are available at:https://github.com/Jinec98/MAE3D.

Since point clouds acquired by scanners inevitably contain noise, recovering a clean version from a noisy point cloud is essential for further 3D geometry processing applications. Several data-driven approaches have been recently introduced to overcome the drawbacks of traditional filtering algorithms, such as less robust preservation of sharp features and tedious tuning for multiple parameters. Most of these methods achieve filtering by directly regressing the position/displacement of each point, which may blur detailed features and is prone to uneven distribution. In this article, we propose a novel data-driven method that explores the implicit fields. Our assumption is that the given noisy points implicitly define a surface, and we attempt to obtain a point's movement direction and distance separately based on the predicted signed distance fields (SDFs). Taking a noisy point cloud as input, we first obtain a consistent alignment by incorporating the global points into local patches. We then feed them into an encoder-decoder structure and predict a 7D vector consisting of SDFs. Subsequently, the distance can be obtained directly from the first element in the vector, and the movement direction can be obtained by computing the gradient descent from the last six elements (i.e., six surrounding SDFs). We finally obtain the filtered results by moving each point with its predicted distance along its movement direction. Our method can produce feature-preserving results without requiring explicit normals. Experiments demonstrate that our method visually outperforms state-of-the-art methods and generally produces better quantitative results than position-based methods (both learning and non-learning).

Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create smaller point sets locally and mainly focus on extracting local features through GCNs, while ignoring the relationship between point sets. In this paper, we propose the Dynamic Hop Graph Convolution Network (DHGCN) for explicitly learning the contextual relationships between the voxelized point parts, which are treated as graph nodes. Motivated by the intuition that the contextual information between point parts lies in the pairwise adjacent relationship, which can be depicted by the hop distance of the graph quantitatively, we devise a novel self-supervised part-level hop distance reconstruction task and design a novel loss function accordingly to facilitate training. In addition, we propose the Hop Graph Attention (HGA), which takes the learned hop distance as input for producing attention weights to allow edge features to contribute distinctively in aggregation. Eventually, the proposed DHGCN is a plug-and-play module that is compatible with point-based backbone networks. Comprehensive experiments on different backbones and tasks demonstrate that our self-supervised method achieves state-of-the-art performance. Our source codes are available at: https://github.com/Jinec98/DHGCN.

In this paper, we present PCoTTA, an innovative, pioneering framework for Continual Test-Time Adaptation (CoTTA) in multi-task point cloud understanding, enhancing the model's transferability towards the continually changing target domain. We introduce a multi-task setting for PCoTTA, which is practical and realistic, handling multiple tasks within one unified model during the continual adaptation. Our PCoTTA involves three key components: automatic prototype mixture (APM), Gaussian Splatted feature shifting (GSFS), and contrastive prototype repulsion (CPR). Firstly, APM is designed to automatically mix the source prototypes with the learnable prototypes with a similarity balancing factor, avoiding catastrophic forgetting. Then, GSFS dynamically shifts the testing sample toward the source domain, mitigating error accumulation in an online manner. In addition, CPR is proposed to pull the nearest learnable prototype close to the testing feature and push it away from other prototypes, making each prototype distinguishable during the adaptation. Experimental comparisons lead to a new benchmark, demonstrating PCoTTA's superiority in boosting the model's transferability towards the continually changing target domain. Our source code is available at: https://github.com/Jinec98/PCoTTA.

Abstract In computer vision, the task of 3D reconstruction from monocular sparse images poses significant challenges, particularly in the field of animal modelling. The diverse morphology of animals, their varied postures, and the variable conditions of image acquisition significantly complicate the task of accurately reconstructing their 3D shape and pose from a monocular image. To address these complexities, we propose S‐LASSIE, a novel technique for 3D reconstruction of quadrupeds from monocular sparse images. It requires only 10–30 images of similar breeds for training. To effectively mitigate depth ambiguities inherent in monocular reconstructions, S‐LASSIE employs a multi‐angle projection loss function. In addition, our approach, which involves fusion and smoothing of bone structures, resolves issues related to disjointed topological structures and uneven connections at junctions, resulting in 3D models with comprehensive topologies and improved visual fidelity. Our extensive experiments on the Pascal‐Part and LASSIE datasets demonstrate significant improvements in keypoint transfer, overall 2D IOU and visual quality, with an average keypoint transfer and overall 2D IOU of 59.6% and 86.3%, respectively, which are superior to existing techniques in the field.

ABSTRACT Painting is a complex and creative process that involves the use of various drawing skills to create artworks. The concept of training artificial intelligence models to imitate this process is referred to as neural painting. To enable ordinary people to engage in the process of painting, we propose PainterAR, a novel interface that renders any paintings stroke‐by‐stroke in an immersive and realistic augmented reality (AR) environment. PainterAR is composed of two components: the neural painting model and the AR interface. Regarding the neural painting model, unlike previous models, we introduce the Kullback–Leibler divergence to replace the original Wasserstein distance existed in the baseline paint transformer model, which solves an important problem of encountering different scales of strokes (big or small) during painting. We then design an interactive AR interface, which allows users to upload an image and display the creation process of the neural painting model on the virtual drawing board. Experiments demonstrate that the paintings generated by our improved neural painting model are more realistic and vivid than previous neural painting models. The user study demonstrates that users prefer to control the painting process interactively in our AR environment.

Abstract Simulation of swarm motion is a crucial research area in computer graphics and animation, and is widely used in a variety of applications such as biological behavior research, robotic swarm control, and the entertainment industry. In this paper, we address the challenges of preserving structural relations between the individuals in swarm flight simulations by proposing an innovative motion control framework that utilizes a graph‐based hierarchy to illustrate patterns within a swarm and allows the swarm to perform flight motions along externally specified paths. In addition, this study designs motion propagation strategies with different focuses for varied application scenarios, analyzes the effects of information transfer latencies on pattern preservation under these strategies, and optimizes the control algorithms at the mathematical level. This study not only establishes a complete set of control methods for group flight simulations, but also has excellent scalability, which can be combined with other techniques in this field to provide new solutions for group behavior simulations.

Animal behaviour can reflect the health and physiological stage of the animal. Animal behaviour recognition is a vital part of automated farming systems. Although image-based deep learning algorithms can accurately identify animal behaviour, the lack of data on animal abnormal behaviour makes the practical deployment of models of limited significance. At the same time, the ageing of farm monitoring equipment is also a key factor hindering automated farming. This paper constructs a sheep abnormal behaviour dataset ABSB to address these issues and proposes a lightweight real-time multi-sheep abnormal behaviour detection model YOLOv7-Lrab based on the YOLOv7-tiny network. The abnormal behaviour dataset includes four normal behaviours: standing, lying, eating and drinking, and three abnormal behaviours: lameness, attack and death. In the proposed YOLOv7-Lrab model, the small target detection layer, Coordinate attention module, SPD-Conv and Mobileone module are added compared to YOLOv7-tiny. The experimental results show that with a 7:3 ratio of training data to test data, 96.5% recognition accuracy and 95.5% recall can be achieved, and the model size is only 4.5MB with fps of 156. The model is compressed to a minimum without loss of accuracy, providing a new idea for deploying deep learning model in practical application scenarios.

While a popular representation of 3D data, point clouds may contain noise and need filtering before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output. To address this problem, this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. The key idea is to incorporate a repulsion term with a data term in energy minimization. The repulsion term is responsible for the point distribution, while the data term aims to approximate the noisy surfaces while preserving geometric features. This method is capable of handling models with fine-scale features and sharp features. Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.

Abstract In this article, we propose a point cloud synthesis method based on stochastic differential equations. We view the point cloud generation process as smoothly transforming from a known prior distribution toward the high‐likelihood shape by point‐level denoising. We introduce a conditional corrector sampler to improve the quality of point clouds. By leveraging Markov chain Monte Carlo sample, our method can synthesize realistic point clouds. We additionally prove that our approach can be trained in an auto‐encoding fashion and reconstruct the point cloud faithfully. Furthermore, our model can be extended on a downstream application of point cloud completion. Experimental results demonstrate the effectiveness and efficiency of our method.

Abstract Facial recognition technology and related research have matured over time, but research in the field of individual animal recognition is still very limited. Therefore, this article focuses on the identification of cashmere goats with similar characteristics. First, the single shot multibox detector network was used to process the dataset. Next, transfer learning was applied to learn the characteristics of the goats, as well as the loss function is composed of Triplet Loss and Label Smoothing CrossEntropy Loss function. The result of Label Smoothing CrossEntropy Loss function is fused by multiple different branches, which is convenient for classification. We added a small number of images of 24 different breeds of sheep to each cashmere goat dataset with different ID to promote the distance between training individuals, and then used the trained model to find the number of goats with the lowest recognition accuracy. The Cycle‐Consistent Adversarial Network (Cycle‐GAN) learned the goat dataset with a high error rate in individual identification. Unlike previous studies using the Cycle‐GAN, we took the novel approach of using this network to learn and combine the features seen in photos of cashmere goats. Since the learned features were all observed in the same goats, this method achieved better results in learning the features of the goats. Finally, we found that recognition can be performed on this data with an accuracy of 93.75%. These results suggest that identification based on deep learning has a high accuracy rate, as well as great value in identifying individual cashmere goats.

In-place gesture-based virtual locomotion techniques enable users to control their viewpoint and intuitively move in the 3D virtual environment. A key research problem is to accurately and quickly recognize in-place gestures, since they can trigger specific movements of virtual viewpoints and enhance user experience. However, to achieve real-time experience, only short-term sensor sequence data (up to about 300ms, 6 to 10 frames) can be taken as input, which actually affects the classification performance due to limited spatiotemporal information. In this paper, we propose a novel long-term memory augmented network for in-place gestures classification. It takes as input both short-term gesture sequence samples and their corresponding long-term sequence samples that provide extra relevant spatio-temporal information in the training phase. We store long-term sequence features with an external memory queue. In addition, we design a memory augmented loss to help cluster features of the same class and push apart features from different classes, thus enabling our memory queue to memorize more relevant long-term sequence features. In the inference phase, we input only short-term sequence samples to recall the stored features accordingly, and fuse them together to predict the gesture class. We create a large-scale in-place gestures dataset from 25 participants with 11 gestures. Our method achieves a promising accuracy of 95.1% with a latency of 192ms, and an accuracy of 97.3% with a latency of 312ms, and is demonstrated to be superior to recent in-place gesture classification techniques. User study also validates our approach. Our source code and dataset will be made available to the community.

Abstract As the generation of realistic bas‐relief models from 2D images suffers from insufficient 3D depth information and severe under‐constraint, in this article, we propose a new framework for bas‐relief modeling based on 2D decorative images, which adopts conditional generative adversarial network to infer the normal information of the decorative bas‐reliefs from the greyscale information extracted from the images. For the variety of models, we extract the internal structure information through the saliency detection method based on scene perception, and use the transfer process based on the optimized texture synthesis algorithm to complete the normal editing from the source normal map to the new one, which can diversify and control the structure and detailed information of existed normal map. Finally, we adopt a bas‐relief reconstruction approach based on domain transfer recursive filter and surface from gradient to recover 2.5D information from predicted and transferred normal maps. Experiments on various model examples demonstrate the efficiency and diversity of the proposed method in reconstructing bas‐relief models from a single decorative image.

Walking in place for moving through virtual environments has attracted noticeable attention recently. Recent attempts focused on training a classifier to recognize certain patterns of gestures (e.g., standing, walking, etc) with the use of neural networks like CNN or LSTM. Nevertheless, they often consider very few types of gestures and/or induce less desired latency in virtual environments. In this paper, we propose a novel framework for accurate and efficient classification of in-place gestures. Our key idea is to treat several consecutive frames as a “point cloud”. The HMD and two VIVE trackers provide three points in each frame, with each point consisting of 12-dimensional features (i.e., three-dimensional position coordinates, velocity, rotation, angular velocity). We create a dataset consisting of 9 gesture classes for virtual in-place locomotion. In addition to the supervised point-based network, we also take unsupervised domain adaptation into account due to inter-person variations. To this end, we develop an end-to-end joint framework involving both a supervised loss for supervised point learning and an unsupervised loss for unsupervised domain adaptation. Experiments demonstrate that our approach generates very promising outcomes, in terms of high overall classification accuracy (95.0%) and real-time performance (192ms latency). We will release our dataset and source code to the community.

Abstract It is significant to achieve automatic arrangement for bas‐relief layout which can be noticeably more efficient than the time‐consuming manual process. In fact, nearly none work has been reported in terms of bas‐relief layout arrangement. In this paper, we propose a novel approach to tackle this problem. Specifically, we first identify the evaluation indicators to account for different aesthetic factors, and model the goodness of each indicator. We then cast the bas‐relief layout as a combinatorial optimization problem based on those evaluation indicators and a geometric mean model. The contribution of this paper is to propose an objective function for bas‐relief layout and apply simulated annealing algorithm for optimization. Experiments show that our method is effective, in terms of layout arrangement for bas‐relief generation. In addition, this method can synthesize a few models arrangement and investigate which evaluation indicators will affect the aesthetic perception of the bas‐relief.

Abstract For surgery simulation application, a high‐quality anatomical model is very important not only for rendering but also for physics simulation. CT and MRI reconstructed model has no surface parameterization attribute so texture‐based materials cannot be applied for rendering. Anatomical models on the digital market are efficient options but most can only be used for visualization because of the nonmanifold geometric degeneracies. We proposed a simulation ready model generation pipeline that can convert a nonmanifold polygonal surface mesh into a degeneracy free surface mesh (simulation ready state) while preserving the original model's surface parameterization attribute. Our pipeline includes two stages. The first stage is a voxelization and remesh based simulation ready model generation pipeline, which can keep the shape of the original three‐dimensional surface model meanwhile eliminate the nonmanifold geometry. The second stage is the main contribution of this article. A cutting‐based surface mesh parameterization transfer algorithm is proposed which can transfer the original surface parameterization (UV mapping especially the UV seam) to the simulation ready model. A detailed comparison with existing pipelines is made to show that our pipeline can achieve surface parameterization preservation feature and is more suitable for improving the efficiency of virtual surgery production.

Abstract In this paper, we make two contributions. The first is to propose a new keyframe extraction algorithm, which reduces the keyframe redundancy and reduces the motion sequence reconstruction error. Secondly, a new motion sequence reconstruction method is proposed, which further reduces the error of motion sequence reconstruction. Specifically, we treated the input motion sequence as curves, then the binomial fitting was extended to obtain the points where the slope changes dramatically in the vicinity. Then we took these points as inputs to obtain keyframes by density clustering. Finally, the motion curves were segmented by keyframes and the segmented curves were fitted by binomial formula again to obtain the binomial parameters for motion reconstruction. Experiments show that our methods outperform existing techniques, in terms of reconstruction error.

Sunken relief is an art form whereby the depicted shapes are sunk into a given flat plane with a shallow overall depth. In this paper, we propose an efficient sunken relief generation algorithm based on a single image by the technique of feature line enhancement. Our method starts from a single image. First, we smoothen the image with morphological operations such as opening and closing operations and extract the feature lines by comparing the values of adjacent pixels. Then we apply unsharp masking to sharpen the feature lines. After that, we enhance and smoothen the local information to obtain an image with less burrs and jaggies. Differential operations are applied to produce the perceptive relief-like images. Finally, we construct the sunken relief surface by triangularization which transforms two-dimensional information into a three-dimensional model. The experimental results demonstrate that our method is simple and efficient.

Human motion prediction is a challenging problem due to the complicated human body constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN, CNN or fully connected networks to learn the motion features which do not fully exploit the hierarchical structure of human anatomy. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. The new network is more efficient compared to the existing deep learning models with respect to size and speed. We train the generic model on Human3.6M and CMU benchmark and conduct extensive experiments. The qualitative and quantitative results show that our model outperforms the state-of-the-art methods in both short-term prediction and long-term prediction.

Abstract This paper presents a novel approach for bas‐relief generation and synthesis. In contrast to previous methods, we divide this problem into two parts: the selection of the best view and arrangement of the relief layout. Taking these into account, we incorporate the visual saliency and photographic composition rules into the bas‐relief generation. Additionally, a nonlinear compression function is used to compress the models, and finally, we implement surface parameterization by directly manipulating the mesh triangles to generate curved surface bas‐relief. We validate our approach through a variety of models. The results indicate that the proposed approach is effective to adapt different types of target surface with topology unchanged. Comparing with conventional methods, our approach is able to effectively produce bas‐relief with a reasonable layout and distinct details.

This paper aims to automate the process of generating a meaningful single still image from a temporal input of scene sequences. The success of our extraction relies on evaluating the optimal pose of characters selection, which should maximize the information conveyed. We define the information entropy of the still image candidates as the evaluation criteria.

Abstract This paper simulates the behaviour of collective transport where a group of ants transports an object in a cooperative fashion. Different from humans, the task coordination of collective transport, with ants, is not achieved by direct communication between group individuals, but through indirect information transmission via mechanical movements of the object. This paper proposes a stochastic probability model to model the decision‐making procedure of group individuals and trains a neural network via reinforcement learning to represent the force policy. Our method is scalable to different numbers of individuals and is adaptable to users' input, including transport trajectory, object shape, external intervention, etc. Our method can reproduce the characteristic strategies of ants, such as realign and reposition. The simulations show that with the strategy of reposition, the ants can avoid deadlock scenarios during the task of collective transport.

Abstract With the development of virtual reality technology, surgery simulation has become an effective way to train the operation skills for surgeons. Soft tissue dissection, as one of the most frequently performed operations in surgery, is indispensable to an immersive and high‐fidelity surgery simulator. Energized dissection tools are much more commonly used than the traditional sharp scalpels for patient safety. Unfortunately, the interaction of such tools with the soft tissues has been largely ignored in the research of surgical simulators. In this paper, we have proposed an energized soft tissue dissection model. We categorize the soft tissues into three types (fascia, membrane, and fat) and simulate their physical property accordingly. The dissection algorithm we propose employs an edge‐based structure, which offers an effective mechanism for the generation of incisions dissected with energized tools. The mesh topology will not be changed when it is dissected by an energized tool, rather it is controlled by the heat transfer model. Our dissection method is highly compatible and efficient to the physically based simulation resolved by a pre‐factorized linear system. Copyright © 2016 John Wiley & Sons, Ltd.

Collision detection and resolution are of great importance to physically based animation. Real time responses are essential for many applications, which largely rely on the efficiency of localising the potentially colliding geometry and calculating the polygon intersections. It is an extremely heavy computation task using the existing polygon based methods, especially for deformable objects. To improve this issue, we present an implicit circumsphere based collision detection and resolution method for deformable objects which takes into consideration both local geometry features and the material properties. Our method approximates the mesh in question with an implicit circumsphere surface, which is used to perform finest level collision detection and resolution instead of the original polygonal mesh. The dynamic deformation as a result of collision is determined by both the geometry and the material properties of the surface. Due to the simplicity of sphere overlap test, our method is not only computationally efficient, but also stable and comparatively accurate, outperforming the existing methods in overall performance. Our implicit circumsphere method can also provide better prevention to collision tunnelling than existing methods. Besides, this method is compatible with all existing broad phase and narrow phase collision query techniques.

To better capture the shapes as well as the rich dynamics of hair, image based modelling techniques have been developed for reconstructing their 3D geometry and important visual features. Most hair images contain inevitable noises which impair reconstructed hair models. Therefore we propose to pre-process hair images and provide an orientation map of hair strands to enhance the follow-on modelling. To demonstrate the usage of pre-processing techniques, we apply our pre-processing results for bas-relief stylisation and modelling of hair from image inputs. We compare different techniques to estimate hair orientations, adopting four types of filter mechanisms. Our analysis of their performance sheds insight on designing a suitable pre-processing technique for hair reconstruction from images. Several examples of bas-relief creation validate the effectiveness of the proposed approach.

Abstract In this paper, we present an overview of the achievements accomplished to date in the field of computer‐aided relief generation. We delineate the problem, classify different solutions, analyse similarities, investigate developments and review the approaches according to their particular relative strengths and weaknesses. Moreover, we describe remaining challenges and point out prospective extensions. In consequence, this survey is addressed to both researchers and artists, through providing valuable insights into the theory behind the different concepts in this field and augmenting the options available among the methods presented with regard to practical application.