Sketching-out virtual humans: A smart interface for human modelling and animation

In this paper, we present a fast and intuitive interface for sketching out 3D virtual humans and animation. The user draws stick figure key frames first and chooses one for “fleshing-out” with freehand body contours. The system automatically constructs a plausible 3D skin surface from the rendered figure, and maps it onto the posed stick figures to produce the 3D character animation. A “creative model-based method” is developed, which performs a human perception process to generate 3D human bodies of various body sizes, shapes and fat distributions. In this approach, an anatomical 3D generic model has been created with three distinct layers: skeleton, fat tissue, and skin. It can be transformed sequentially through rigid morphing, fatness morphing, and surface fitting to match the original 2D sketch. An auto-beautification function is also offered to regularise the 3D asymmetrical bodies from users’ imperfect figure sketches. Our current system delivers character animation in various forms, including articulated figure animation, 3D mesh model animation, 2D contour figure animation, and even 2D NPR animation with personalised drawing styles. The system has been formally tested by various users on Tablet PC. After minimal training, even a beginner can create vivid virtual humans and animate them within minutes

Sketching-out virtual humans: From 2d storyboarding to immediate 3d character animation

Virtual beings are playing a remarkable role in today’s public entertainment, while ordinary users are still treated as audiences due to the lack of appropriate expertise, equipment, and computer skills. In this paper, we present a fast and intuitive storyboarding interface, which enables users to sketch-out 3D virtual humans, 2D/3D animations, and character intercommunication. We devised an intuitive “stick figurefleshing-outskin mapping” graphical animation pipeline, which realises the whole process of key framing, 3D pose reconstruction, virtual human modelling, motion path/timing control, and the final animation synthesis by almost pure 2D sketching. A “creative model-based method” is developed, which emulates a human perception process, to generate the 3D human bodies of variational sizes, shapes, and fat distributions. Meanwhile, our current system also supports the sketch-based crowd animation and the storyboarding of the 3D multiple character intercommunication. This system has been formally tested by various users on Tablet PC. After minimal training, even a beginner can create vivid virtual humans and animate them within minutes

Sketch-based virtual human modelling and animation

Animated virtual humans created by skilled artists play a remarkable role in today’s public entertainment. However, ordinary users are still treated as audiences due to the lack of appropriate expertise, equipment, and computer skills. We developed a new method and a novel sketching interface, which enable anyone who can draw to “sketch-out” 3D virtual humans and animation. We devised a “Stick FigureFleshing-outSkin Mapping” graphical pipeline, which decomposes the complexity of figure drawing and considerably boosts the modelling and animation efficiency. We developed a gesture-based method for 3D pose reconstruction from 2D stick figure drawings. We investigated a “Creative Model-based Method”, which performs a human perception process to transfer users’ 2D freehand sketches into 3D human bodies of various body sizes, shapes and fat distributions. Our current system supports character animation in various forms including articulated figure animation, 3D mesh model animation, and 2D contour/NPR animation with personalised drawing styles. Moreover, this interface also supports sketch-based crowd animation and 2D storyboarding of 3D multiple character interactions. A preliminary user study was conducted to support the overall system design. Our system has been formally tested by various users on Tablet PC. After minimal training, even a beginner can create vivid virtual humans and animate them within minutes

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

Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition

Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition