In the era of the digital economy, the exploration of useful knowledge from data streams has garnered significant attention due to its wide-ranging applications. However, the rapid and infinite nature of data streams poses challenges for efficiently mining high utility sequential patterns, including strong spatio-temporal constraints and the combinatorial explosion of sequence data search spaces. To address this and adapt to a variety of application scenarios, this paper delves into the investigation and design of an efficient algorithm for high utility sequential pattern mining over data streams based on the sliding window model (HUSP_DS). This algorithm utilizes a projection mechanism within a sliding window to recursively search for all interesting patterns. Additionally, it introduces a novel structure called the dynamic utility index table, which stores information such as the utility and index positions of data stream sequences. Notably, this structure proves highly effective in recursive search processes and utility updates. Comprehensive experimentation, conducted on both real-world and synthetic datasets, have shown that the superior performance of the HUSP_DS algorithm compared to state-of-the-art algorithms. This superiority is particularly evident in terms of temporal and spatial efficiency. Furthermore, the algorithm demonstrates suitability for mining sliding windows of arbitrary sizes, showcasing stable scalability.

Detecting symmetrical properties is a fundamental task in 3D shape analysis. In the case of a 3D model with planar symmetries, each point has a corresponding mirror point w.r.t. a symmetry plane, and the correspondences remain invariant under any arbitrary Euclidean transformation. Our proposed method, E3Sym, aims to detect planar reflective symmetry in an unsupervised and end-to-end manner by leveraging E(3) invariance. E3Sym establishes robust point correspondences through the use of E(3) invariant features extracted from a lightweight neural network, from which the dense symmetry prediction is produced. We also introduce a novel and efficient clustering algorithm to aggregate the dense prediction and produce a detected symmetry set, allowing for the detection of an arbitrary number of planar symmetries while ensuring the method remains differentiable for end-to-end training. Our method also possesses the ability to infer reasonable planar symmetries from incomplete shapes, which remains challenging for existing methods. Extensive experiments demonstrate that E3Sym is both effective and robust, outperforming state-of-the-art methods.

Sketches, which are simple and concise, have been used in recent deep image synthesis methods to allow intuitive generation and editing of facial images. However, it is nontrivial to extend such methods to video editing due to various challenges, ranging from appropriate manipulation propagation and fusion of multiple editing operations to ensure temporal coherence and visual quality. To address these issues, we propose a novel sketch-based facial video editing framework, in which we represent editing manipulations in latent space and propose specific propagation and fusion modules to generate high-quality video editing results based on StyleGAN3. Specifically, we first design an optimization approach to represent sketch editing manipulations by editing vectors, which are propagated to the whole video sequence using a proper strategy to cope with different editing needs. Specifically, input editing operations are classified into two categories: temporally consistent editing and temporally variant editing. The former (e.g., change of face shape) is applied to the whole video sequence directly, while the latter (e.g., change of facial expression or dynamics) is propagated with the guidance of expression or only affects adjacent frames in a given time window. Since users often perform different editing operations in multiple frames, we further present a region-aware fusion approach to fuse diverse editing effects. Our method supports video editing on facial structure and expression movement by sketch, which cannot be achieved by previous works. Both qualitative and quantitative evaluations show the superior editing ability of our system to existing and alternative solutions.

Retrieving occlusion relation among objects in a single image is challenging due to sparsity of boundaries in image. We observe two key issues in existing works: firstly, lack of an architecture which can exploit the limited amount of coupling in the decoder stage between the two subtasks, namely occlusion boundary extraction and occlusion orientation prediction, and secondly, improper representation of occlusion orientation. In this paper, we propose a novel architecture called Occlusion-shared and Path-separated Network (OPNet), which solves the first issue by exploiting rich occlusion cues in shared high-level features and structured spatial information in task-specific low-level features. We then design a simple but effective orthogonal occlusion representation (OOR) to tackle the second issue. Our method surpasses the state-of-the-art methods by 6.1%/8.3% Boundary-AP and 6.5%/10% Orientation-AP on standard PIOD/BSDS ownership datasets. Code is available at https://github.com/fengpanhe/MT-ORL.

Recent facial image synthesis methods have been mainly based on conditional generative models. Sketch-based conditions can effectively describe the geometry of faces, including the contours of facial components, hair structures, as well as salient edges (e.g., wrinkles) on face surfaces but lack effective control of appearance, which is influenced by color, material, lighting condition, etc. To have more control of generated results, one possible approach is to apply existing disentangling works to disentangle face images into geometry and appearance representations. However, existing disentangling methods are not optimized for human face editing, and cannot achieve fine control of facial details such as wrinkles. To address this issue, we propose DeepFaceEditing, a structured disentanglement framework specifically designed for face images to support face generation and editing with disentangled control of geometry and appearance. We adopt a local-to-global approach to incorporate the face domain knowledge: local component images are decomposed into geometry and appearance representations, which are fused consistently using a global fusion module to improve generation quality. We exploit sketches to assist in extracting a better geometry representation, which also supports intuitive geometry editing via sketching. The resulting method can either extract the geometry and appearance representations from face images, or directly extract the geometry representation from face sketches. Such representations allow users to easily edit and synthesize face images, with decoupled control of their geometry and appearance. Both qualitative and quantitative evaluations show the superior detail and appearance control abilities of our method compared to state-of-the-art methods.

Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit surfaces, etc. The performance achieved in different applications largely depends on the representation used, and there is no unique representation that works well for all applications. Therefore, in this survey, we review recent developments in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations for different applications. We also present existing datasets in these representations and further discuss future research directions.

In this paper, we propose a photo stylistic brush, an automatic robust style transfer approach based on soft segmentation-guided bipartite graph. A two-step bipartite graph algorithm with different granularity levels is employed to aggregate pixels into superpixel and find their correspondences. In the first step, with the extracted hierarchical features, a bipartite graph is constructed to describe the content similarity for pixel partition to produce superpixels. In the second step, superpixels in the input/reference image are rematched to form a new soft segmentation-guided bipartite graph, and superpixel-level correspondences are generated by a bipartite matching. Finally, the refined correspondence guides our approach to perform the transfer in a decorrelated color space. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method for transferring various styles of exemplar images, even for some challenging cases, such as night images.

ABSTRACT Motion planning is an important problem in character animation and interactive simulation. However, few planning methods have considered domain‐specific knowledge that governs the agent's behaviors, and none of them is capable of planning the interactive task in which the agent interacts with the objects in the virtual environment. This paper presents a novel method to plan the interactive task based on Q‐learning for intelligent characters. The approach can be described as a three‐phase framework: data preprocessing phase, controller learning phase, and motion‐synthesis phase. In the data preprocessing phase, we abstract the motion clips as high‐level behaviors and construct the interactive behavior graph (IBG) to define the interactive capabilities of the agent in terms of interactive features. For the controller training phase, with IBG, Q‐learning algorithm is employed to train the control policy in the discrete domain with interactive features. In the motion‐synthesis phase, the optimal motion sequences can be generated by following the policy to accomplish the interactive task finally. The experimental results demonstrate that the uniform framework can generate reasonable and realistic motion sequences to plan interactive task in complex environment. Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents a hybrid approach based on the continuum model proposed by Treuille et al.. Compared to the original method, our solution is well suited for complex environment. We first present an environment structure and a corresponding discretization scheme that help us to organize and simulate crowds in large-scale scenarios. Second, additional discomforts around obstacles are auto-generated for keeping a certain distance between pedestrians and obstacles which is psychologically plausible, and it could obtain smoother trajectory when people move around many obstacles. Thirdly, we propose a technique for density conversion; the density field is dynamically affected by each individual so that it could be adapted to different grid resolution. The experiment results demonstrate that our hybrid solution can perform plausible crowds flow in complex dynamic environments.

Simulating crowds in complex environment is fascinating and challenging, however, modeling of the environment is always neglected in the past, which is one of the essential problems in crowd simulation especially for multilayered complex environment. This paper presents a semantic model for representing the complex environment, where the semantic information is described with a three-tier framework: a geometric level, a semantic level and an application level. Each level contains different maps for different purposes and our approach greatly facilitates the interactions between individuals and virtual environment. And then a modified continuum crowd method is designed to fit the proposed virtual environment model so that realistic behaviors of large dense crowds could be simulated in multilayered complex environments such as buildings and subway stations. Finally, we implement this method and test it in two complex synthetic urban spaces. The experiment results demonstrate that the semantic environment model can provide sufficient and accurate information for crowd simulation in multilayered complex environment.

As it widely exists in applications as diverse as biomechanics, motion capture and robot control, the fundamental problem of articulated skeleton fitting, which is to fit an articulated skeleton to k frames of corresponding raw joint points under the fixed-bone-length constraint, is proposed in this paper. With different parametrization of the articulated skeleton, three types of potential solutions to the above problem are presented and compared. Special cases for this problem, including that generated under joint trajectory smoothness constraints and from weighted joints, are also discussed and analyzed. Based on the joint location parametrization and the sparse matrix techniques, we propose an efficient and accurate articulated skeleton fitting method (JLP2). Elaborate experiments and interesting applications in motion capture are presented to show its effectiveness.

This paper presents a method of pose synthesis based on a low-dimensional space and a set of characteristics of motion learned from examples. This method consists of two phases: learning and synthesis. In the learning phase, a low-dimensional and discrete representation of the space of natural poses is constructed by using a self organizing map (SOM). Meanwhile, a set of matrices is extracted from the motion data. These matrices describe how the poses change with the end-effectors' positions, and play a key role in synthesizing natural looking results. In the synthesis phase, a lightweight algorithm based on the learned parameters is used. The synthesis process is very efficient because there is no time-consuming calculation, like numeric optimization or matrix inverting. Compared with other methods, our method not only can produce natural looking poses in real-time, but also works well with constraints positioned in a larger range. We apply our method in applications of interactive pose editing, real-time motion modification, and pose reconstruction from image. The results have proven the robustness and effectiveness of our method