Modeling Sketching Primitives to Support Freehand Drawing Based on Context Awareness

Freehand drawing is an easy and intuitive method for thinking input and output. In sketch based interface, there lack support for natural sketching with drawing cues, like overlapping, overlooping, hatching, etc. which happen frequently in physical pen and paper. In this paper, we analyze some characters of drawing cues in sketch based interface and describe the different types of sketching primitives. An improved sketch information model is given and the idea is to present and record design thinking during freehand drawing process with individuality and diversification. The interaction model based on context is developed which can guide and help new sketch-based interface development. New applications with different context contents can be easily derived from it and developed further. Our approach can support the tasks that are common across applications, requiring the designer to only provide support for the application-specific tasks. It is capable of and applicable for modeling various sketching interfaces and applications. Finally, we illustrate the general operations of the system by examples in different applications

Research on the Problem of Old-Age Care in China From the Perspective of Ethics

The elders are an important driving force for economic development, social progress and national prosperity. The care of the elderly, as a universal world problem, is related to their dignity, human rights and social stability. Since 2000, China has stepped into the threshold of an aging society. With the increasing aging population and the influence of population, economy, culture, politics and other uniquely national conditions, the conflicts and problems over providing for the aged are more complicated and severe. It has become an urgent issue in China how to solve a series of ethical dilemmas of providing for the aged caused by the aging population.Firstly, we use the data of the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) for positivist research, and then adopt the ordered Probit regression model for analysis. Secondly, we present and discuss the existing ethical problems of providing for the aged in society: firstly, the unfairness of allocation of resources in the society for providing for the aged; secondly, the dilution of concept of filial piety in family; thirdly, the weakening of the trend of respecting the elderly in society; last but not least, the lack of good in old-age security system. In view of the above-mentioned ethical problems of providing for the aged in China, we propose some solutions, namely, fairly distributing old-age resources in society; promoting the cultural tradition of filial piety; improving the system of endowment policy. Focusing on the ethical issues of old-age care, this paper provides some ethical thoughts for the solution of them

Novel-view Synthesis and Pose Estimation for Hand-Object Interaction from Sparse Views

Hand-object interaction understanding and the barely addressed novel view synthesis are highly desired in the immersive communication, whereas it is challenging due to the high deformation of hand and heavy occlusions between hand and object. In this paper, we propose a neural rendering and pose estimation system for hand-object interaction from sparse views, which can also enable 3D hand-object interaction editing. We share the inspiration from recent scene understanding work that shows a scene specific model built beforehand can significantly improve and unblock vision tasks especially when inputs are sparse, and extend it to the dynamic hand-object interaction scenario and propose to solve the problem in two stages. We first learn the shape and appearance prior knowledge of hands and objects separately with the neural representation at the offline stage. During the online stage, we design a rendering-based joint model fitting framework to understand the dynamic hand-object interaction with the pre-built hand and object models as well as interaction priors, which thereby overcomes penetration and separation issues between hand and object and also enables novel view synthesis. In order to get stable contact during the hand-object interaction process in a sequence, we propose a stable contact loss to make the contact region to be consistent. Experiments demonstrate that our method outperforms the state-of-the-art methods. Code and dataset are available in project webpage https://iscas3dv.github.io/HO-NeRF

SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement.Comment: Main paper (11 pages, 6 figures) and Supplemental document (11 pages, 11 figures). Accepted by VIS 202

SketchGAN: Joint sketch completion and recognition with generative adversarial network

Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input; however, hand-drawn sketches in common application scenarios are often incomplete, which makes sketch recognition a challenging problem. In this paper, we propose SketchGAN, a new generative adversarial network (GAN) based approach that jointly completes and recognizes a sketch, boosting the performance of both tasks. Specifically, we use a cascade Encode-Decoder network to complete the input sketch in an iterative manner, and employ an auxiliary sketch recognition task to recognize the completed sketch. Experiments on the Sketchy database benchmark demonstrate that our joint learning approach achieves competitive sketch completion and recognition performance compared with the state-of-the-art methods. Further experiments using several sketch-based applications also validate the performance of our method

E-ffective: a visual analytic system for exploring the emotion andeffectiveness of inspirational speeches

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness

SceneSketcher-v2: Fine-grained scene-level sketch-based image retrieval using adaptive GCNs

Sketch-based image retrieval (SBIR) is a long-standing research topic in computer vision. Existing methods mainly focus on category-level or instance-level image retrieval. This paper investigates the fine-grained scene-level SBIR problem where a free-hand sketch depicting a scene is used to retrieve desired images. This problem is useful yet challenging mainly because of two entangled facts: 1) achieving an effective representation of the input query data and scene-level images is difficult as it requires to model the information across multiple modalities such as object layout, relative size and visual appearances, and 2) there is a great domain gap between the query sketch input and target images. We present SceneSketcher-v2, a Graph Convolutional Network (GCN) based architecture to address these challenges. SceneSketcher-v2 employs a carefully designed graph convolution network to fuse the multi-modality information in the query sketch and target images and uses a triplet training process and end-to-end training manner to alleviate the domain gap. Extensive experiments demonstrate SceneSketcher-v2 outperforms state-of-the-art scene-level SBIR models with a significant margin

SpeciFingers

The inadequate use of finger properties has limited the input space of touch interaction. By leveraging the category of contacting fingers, finger-specific interaction is able to expand input vocabulary. However, accurate finger identification remains challenging, as it requires either additional sensors or limited sets of identifiable fingers to achieve ideal accuracy in previous works. We introduce SpeciFingers, a novel approach to identify fingers with the capacitive raw data on touchscreens. We apply a neural network of an encoder-decoder architecture, which captures the spatio-temporal features in capacitive image sequences. To assist users in recovering from misidentification, we propose a correction mechanism to replace the existing undo-redo process. Also, we present a design space of finger-specific interaction with example interaction techniques. In particular, we designed and implemented a use case of optimizing the performance in pointing on small targets. We evaluated our identification model and error correction mechanism in our use case