Deep Learning for Free-Hand Sketch: A Survey

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.Comment: This paper is accepted by IEEE TPAM

Integration of sketch-based ideation and 3D modeling with CAD systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis is concerned with the study of how sketch-based systems can be improved to enhance idea generation process in conceptual design stage. It is also concerned with achieving a kind of integration between sketch-based systems and CAD systems to complete the digitization of the design process as sketching phase is still not integrated with other phases due to the different nature of it and the incomplete digitization of sketching phase itself. Previous studies identified three main related issues: sketching process, sketch-based modeling, and the integration between the digitized design phases. Here, the thesis is motivated from the desire to improve sketch-based modeling to support idea generation process but unlike previous studies that only focused on the technical or drawing part of sketching, this thesis attempts to concentrate more on the mental part of the sketching process which play a key role in developing ideas in design. Another motivation of this thesis is to produce a kind of integration between sketch-based systems and CAD systems to enable 3D models produced by sketching to be edited in detailed design stage. As such, there are two main contributions have been addressed in this thesis. The first contribution is the presenting of a new approach in designing sketch-based systems that enable more support for idea generation by separating thinking and developing ideas from the 3D modeling process. This kind of separation allows designers to think freely and concentrate more on their ideas rather than 3D modeling. the second contribution is achieving a kind of integration between gesture-based systems and CAD systems by using an IGES file in exchanging data between systems and a new method to organize data within the file in an order that make it more understood by feature recognition embedded in commercial CAD systems.This study is funded by the Ministry of Higher Education of Egypt

Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

A new paradigm based on agents applied to free-hand sketch recognition

Important advances in natural calligraphic interfaces for CAD (Computer Aided Design) applications are being achieved, enabling the development of CAS (Computer Aided Sketching) devices that allow facing up to the conceptual design phase of a product. Recognizers play an important role in this field, allowing the interpretation of the user’s intention, but they still present some important lacks. This paper proposes a new recognition paradigm using an agent-based architecture that does not depend on the drawing sequence and takes context information into account to help decisions. Another improvement is the absence of operation modes, that is, no button is needed to distinguish geometry from symbols or gestures, and also “interspersing” and “overtracing” are accomplishedThe Spanish Ministry of Science and Education and the FEDER Funds, through the CUESKETCH project (Ref. DPI2007-66755-C02-01), partially supported this work.Fernández Pacheco, D.; Albert Gil, FE.; Aleixos Borrás, MN.; Conesa Pastor, J. (2012). A new paradigm based on agents applied to free-hand sketch recognition. Expert Systems with Applications. 39(8):7181-7195. https://doi.org/10.1016/j.eswa.2012.01.063S7181719539

Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning

As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+, which is a deep-learning algorithm that takes the input of a single free-hand sketch and produces a complete and high-fidelity model that matches the sketch input. The neural network has view- and structural-awareness enabled by a Shape Discriminator (SD) and a Stroke Enhancement Module (SEM), which overcomes the limitations of sparsity and ambiguity of the sketches. The network design also brings high robustness to partial sketch input in industrial applications.Our approach has undergone extensive experiments, demonstrating its state-of-the-art (SOTA) performance on both synthetic and real-world datasets. These results validate the effectiveness and superiority of our method compared to existing techniques. We have demonstrated the conversion of free-hand sketches into physical 3D objects using additive manufacturing. We believe that our approach has the potential to accelerate product design and democratize customized manufacturing

To Draw or Not to Draw: Recognizing Stroke-Hover Intent in Gesture-Free Bare-Hand Mid-Air Drawing Tasks

Over the past several decades, technological advancements have introduced new modes of communication with the computers, introducing a shift from traditional mouse and keyboard interfaces. While touch based interactions are abundantly being used today, latest developments in computer vision, body tracking stereo cameras, and augmented and virtual reality have now enabled communicating with the computers using spatial input in the physical 3D space. These techniques are now being integrated into several design critical tasks like sketching, modeling, etc. through sophisticated methodologies and use of specialized instrumented devices. One of the prime challenges in design research is to make this spatial interaction with the computer as intuitive as possible for the users. Drawing curves in mid-air with fingers, is a fundamental task with applications to 3D sketching, geometric modeling, handwriting recognition, and authentication. Sketching in general, is a crucial mode for effective idea communication between designers. Mid-air curve input is typically accomplished through instrumented controllers, specific hand postures, or pre-defined hand gestures, in presence of depth and motion sensing cameras. The user may use any of these modalities to express the intention to start or stop sketching. However, apart from suffering with issues like lack of robustness, the use of such gestures, specific postures, or the necessity of instrumented controllers for design specific tasks further result in an additional cognitive load on the user. To address the problems associated with different mid-air curve input modalities, the presented research discusses the design, development, and evaluation of data driven models for intent recognition in non-instrumented, gesture-free, bare-hand mid-air drawing tasks. The research is motivated by a behavioral study that demonstrates the need for such an approach due to the lack of robustness and intuitiveness while using hand postures and instrumented devices. The main objective is to study how users move during mid-air sketching, develop qualitative insights regarding such movements, and consequently implement a computational approach to determine when the user intends to draw in mid-air without the use of an explicit mechanism (such as an instrumented controller or a specified hand-posture). By recording the user’s hand trajectory, the idea is to simply classify this point as either hover or stroke. The resulting model allows for the classification of points on the user’s spatial trajectory. Drawing inspiration from the way users sketch in mid-air, this research first specifies the necessity for an alternate approach for processing bare hand mid-air curves in a continuous fashion. Further, this research presents a novel drawing intent recognition work flow for every recorded drawing point, using three different approaches. We begin with recording mid-air drawing data and developing a classification model based on the extracted geometric properties of the recorded data. The main goal behind developing this model is to identify drawing intent from critical geometric and temporal features. In the second approach, we explore the variations in prediction quality of the model by improving the dimensionality of data used as mid-air curve input. Finally, in the third approach, we seek to understand the drawing intention from mid-air curves using sophisticated dimensionality reduction neural networks such as autoencoders. Finally, the broad level implications of this research are discussed, with potential development areas in the design and research of mid-air interactions

An investigation on the framework of dressing virtual humans

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Realistic human models are widely used in variety of applications. Much research has been carried out on improving realism of virtual humans from various aspects, such as body shapes, hair, and facial expressions and so on. In most occasions, these virtual humans need to wear garments. However, it is time-consuming and tedious to dress a human model using current software packages [Maya2004]. Several methods for dressing virtual humans have been proposed recently [Bourguignon2001, Turquin2004, Turquin2007 and Wang2003B]. The method proposed by Bourguignon et al [Bourguignon2001] can only generate 3D garment contour instead of 3D surface. The method presented by Turquin et al. [Turquin2004, Turquin2007] could generate various kinds of garments from sketches but their garments followed the shape of the body and the side of a garment looked not convincing because of using simple linear interpolation. The method proposed by Wang et al. [Wang2003B] lacked interactivity from users, so users had very limited control on the garment shape.This thesis proposes a framework for dressing virtual humans to obtain convincing dressing results, which overcomes problems existing in previous papers mentioned above by using nonlinear interpolation, level set-based shape modification, feature constraints and so on. Human models used in this thesis are reconstructed from real human body data obtained using a body scanning system. Semantic information is then extracted from human models to assist in generation of 3 dimensional (3D) garments. The proposed framework allows users to dress virtual humans using garment patterns and sketches. The proposed dressing method is based on semantic virtual humans. A semantic human model is a human body with semantic information represented by certain of structure and body features. The semantic human body is reconstructed from body scanned data from a real human body. After segmenting the human model into six parts some key features are extracted. These key features are used as constraints for garment construction.Simple 3D garment patterns are generated using the techniques of sweep and offset. To dress a virtual human, users just choose a garment pattern, which is put on the human body at the default position with a default size automatically. Users are allowed to change simple parameters to specify some sizes of a garment by sketching the desired position on the human body.To enable users to dress virtual humans by their own design styles in an intuitive way, this thesis proposes an approach for garment generation from user-drawn sketches. Users can directly draw sketches around reconstructed human bodies and then generates 3D garments based on user-drawn strokes. Some techniques for generating 3D garments and dressing virtual humans are proposed. The specific focus of the research lies in generation of 3D geometric garments, garment shape modification, local shape modification, garment surface processing and decoration creation. A sketch-based interface has been developed allowing users to draw garment contour representing the front-view shape of a garment, and the system can generate a 3D geometric garment surface accordingly. To improve realism of a garment surface, this thesis presents three methods as follows. Firstly, the procedure of garment vertices generation takes key body features as constraints. Secondly, an optimisation algorithm is carried out after generation of garment vertices to optimise positions of garment vertices. Finally, some mesh processing schemes are applied to further process the garment surface. Then, an elaborate 3D geometric garment surface can be obtained through this series of processing. Finally, this thesis proposes some modification and editing methods. The user-drawn sketches are processed into spline curves, which allow users to modify the existing garment shape by dragging the control points into desired positions. This makes it easy for users to obtain a more satisfactory garment shape compared with the existing one. Three decoration tools including a 3D pen, a brush and an embroidery tool, are provided letting users decorate the garment surface by adding some small 3D details such as brand names, symbols and so on. The prototype of the framework is developed using Microsoft Visual Studio C++,OpenGL and GPU programming