Urban green spaces such as parks and gardens are indispensable in both virtual and real-world environments. Therefore, planning such spaces is highly valuable. While scene synthesis literature has limited interest in this topic, many existing parametric design and procedural content generation approaches can be adapted to generate urban green spaces. However, these approaches heavily rely on manual work or are prone to producing monotonously repeated objects. This paper presents a framework that can automatically plan urban green spaces. Tailored to urban green space design, our framework comprises three steps: road system generation, region type planning, and model placement. First, it constructs undirected graphs to generate a sound road system for an empty site and divides the space into separate regions. Then it applies a genetic algorithm to plan suitable surface and vegetation for every region. Finally, it places landscape models based on various patterns and adds embellishments to complete an appealing urban green space. Our framework enables the automatic production of urban green spaces. Through extensive experiments, we demonstrate that the generated results are plausible and reasonable.

Retail space planning, arranging store sections and product placements to optimize customer flow and stimulate purchases helps retailers to increase sales and enhances the customer shopping experience. It can be challenging for retailers to arrange numerous products within limited shelf space. This paper introduces StoreSketcher, an interactive tool that assists retailers in planning retail layouts efficiently at macro and micro levels by providing intelligent suggestions. We have extracted commercial relationships between products and categories, built spatial rules for commercial objects, and developed an interactive framework for synthesizing retail layouts. When the user points to shelf space in the layout, StoreSketcher evaluates the spatial significance of the location and its commercial relation to the surrounding context to present appropriate suggestions. Quantitative experiments demonstrate that StoreSketcher significantly assists in planning well-organized retail layouts. The suggestions provided by StoreSketcher not only boost cross-selling and impulse purchasing for retailers, but also enhance product findability for customers.

This paper presents ScenePalette, a modeling tool that allows users to “draw” 3D scenes interactively by placing objects on a canvas based on their contextual relationship. ScenePalette is inspired by an important intuition which was often ignored in previous work: a real-world 3D scene consists of the contextually reasonable organization of objects, e.g. people typically place one double bed with several subordinate objects into a bedroom instead of different shapes of beds. ScenePalette, abstracts 3D repositories as multiplex networks and accordingly encodes implicit relations between or among objects. Specifically, basic statistics such as co-occurrence, in combination with advanced relations, are used to tackle object relationships of different levels. Extensive experiments demonstrate that the latent space of ScenePalette has rich contexts that are essential for contextual representation and exploration.

As the virtual reality (VR) technology strives to provide immersive and natural user experiences, the challenge of aligning vast virtual environments with limited physical spaces remains significant. This survey comprehensively explores the advancements in redirected walking (RDW) techniques aimed at overcoming spatial constraints in VR. RDW addresses this by subtly manipulating users’ physical movements to allow for seamless navigation within constrained areas. The survey delves into gain perception mechanisms, detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness, thus extending the effective navigable space. Various RDW control algorithms for gain-based RDW are analyzed, highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual disturbances. Furthermore, novel methods extending beyond traditional gain-based techniques are discussed, showcasing innovative approaches that further refine VR interactions. The practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored, alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior patterns. Through a thorough review of existing literature and recent advancements, this survey provides a systematic understanding for researchers, developers, and industry professionals. It underscores the importance of RDW in the future of VR, emphasizing RDW's role in making VR more accessible and practical across various applications, from education and training to therapy and entertainment. The paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

360 ${}^{\circ }$ videos enable viewers to watch freelyfrom different directions but inevitably prevent them from perceiving all the helpful information. To mitigate this problem, picture-in-picture (PIP) guidance was proposed using preview windows to show regions of interest (ROIs) outside the current view range. We identify several drawbacks of this representation and propose a new method for 360 ${}^{\circ }$ film watching called AdaPIP. AdaPIP enhances traditional PIP by adaptively arranging preview windows with changeable view ranges and sizes. In addition, AdaPIP incorporates the advantage of arrow-based guidance by presenting circular windows with arrows attached to them to help users locate the corresponding ROIs more efficiently. We also adapted AdaPIP and Outside-In to HMD-based immersive virtual reality environments to demonstrate the usability of PIP-guided approaches beyond 2D screens. Comprehensive user experiments on 2D screens, as well as in VR environments, indicate that AdaPIP is superior to alternative methods in terms of visual experiences while maintaining a comparable degree of immersion.

In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further post-processing including non-maximum suppression and morphological thinning. In this paper, we demonstrate that the blurry effect arises from the binary cross-entropy loss, and crisp edges could be obtained directly from deep convolutional neural networks. We propose to learn edge maps as the representation of local contrast with a novel local contrast loss. The local contrast is optimized in a stochastic way to focus on specific edge directions. Experiments show that the edge detection network trained with local contrast loss achieves a high accuracy comparable to previous methods and dramatically improves the crispness. We also present several applications of the crisp edges, including image completion, image retrieval, sketch generation, and video stylization.

Photo composition is one of the most important factors in the aesthetics of photographs. As a popular application, composition recommendation for a photo focusing on a specific subject has been ignored by recent deep-learning-based composition recommendation approaches. In this paper, we propose a subject-aware image composition recommendation method, SAC-Net, which takes an RGB image and a binary subject window mask as input, and returns good compositions as crops containing the subject. Our model first determines candidate scores for all possible coarse cropping windows. The crops with high candidate scores are selected and further refined by regressing their corner points to generate the output recommended cropping windows. The final scores of the refined crops are predicted by a final score regression module. Unlike existing methods that need to preset several cropping windows, our network is able to automatically regress cropping windows with arbitrary aspect ratios and sizes. We propose novel stability losses for maximizing smoothness when changing cropping windows along with view changes. Experimental results show that our method outperforms state-of-the-art methods not only on the subject-aware image composition recommendation task, but also for general purpose composition recommendation. We also have designed a multi-stage labeling scheme so that a large amount ofranked pairs can be produced economically. Weuse this scheme to propose the first subject-aware composition dataset SACD, which contains 2777 images, and more than 5 million composition ranked pairs. The SACD dataset is publicly available at https://cg.cs.tsinghua.edu.cn/SACD/.

This paper presents a novel deep neural network for designated point tracking (DPT) in a monocular RGB video, VideoInNet. More concretely, the aim is to track four designated points correlated by a local homography on a textureless planar region in the scene. DPT can be applied to augmented reality and video editing, especially in the field of video advertising. Existing methods predict the location of four designated points without appropriately considering the point correlation. To solve this problem, VideoInNet predicts the motion of the four designated points correlated by a local homography within the heatmap prediction framework. Our network refines the heatmaps of designated points through two stages. On the first stage, we introduce a context-aware and location-aware structure to learn a local homography for the designated plane in a supervised way. On the second stage, we introduce an iterative heatmap refinement module to improve the tracking accuracy. We propose a dataset focusing on textureless planar regions, named ScanDPT, for training and evaluation. We show that the error rate of VideoInNet is about 29% lower than that of the state-of-the-art approach when testing in the first 120 frames of testing videos on ScanDPT.

Humans can naturally and effectively find salient regions in complex scenes. Motivated by thisobservation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multi-modal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.