Computer-assisted animation creation techniques for hair animation and shade, highlight, and shadow

制度:新 ; 報告番号:甲3062号 ; 学位の種類:博士(工学) ; 授与年月日:2010/2/25 ; 早大学位記番号:新532

Sketch-based skeleton-driven 2D animation and motion capture.

This research is concerned with the development of a set of novel sketch-based skeleton-driven 2D animation techniques, which allow the user to produce realistic 2D character animation efficiently. The technique consists of three parts: sketch-based skeleton-driven 2D animation production, 2D motion capture and a cartoon animation filter. For 2D animation production, the traditional way is drawing the key-frames by experienced animators manually. It is a laborious and time-consuming process. With the proposed techniques, the user only inputs one image ofa character and sketches a skeleton for each subsequent key-frame. The system then deforms the character according to the sketches and produces animation automatically. To perform 2D shape deformation, a variable-length needle model is developed, which divides the deformation into two stages: skeleton driven deformation and nonlinear deformation in joint areas. This approach preserves the local geometric features and global area during animation. Compared with existing 2D shape deformation algorithms, it reduces the computation complexity while still yielding plausible deformation results. To capture the motion of a character from exiting 2D image sequences, a 2D motion capture technique is presented. Since this technique is skeleton-driven, the motion of a 2D character is captured by tracking the joint positions. Using both geometric and visual features, this problem can be solved by ptimization, which prevents self-occlusion and feature disappearance. After tracking, the motion data are retargeted to a new character using the deformation algorithm proposed in the first part. This facilitates the reuse of the characteristics of motion contained in existing moving images, making the process of cartoon generation easy for artists and novices alike. Subsequent to the 2D animation production and motion capture,"Cartoon Animation Filter" is implemented and applied. Following the animation principles, this filter processes two types of cartoon input: a single frame of a cartoon character and motion capture data from an image sequence. It adds anticipation and follow-through to the motion with related squash and stretch effect

Pose selection for animated scenes and a case study of bas-relief generation

This paper aims to automate the process of generating a meaningful single still image from a temporal input of scene sequences. The success of our extraction relies on evaluating the optimal pose of characters selection, which should maximize the information conveyed. We define the information entropy of the still image candidates as the evaluation criteria. To validate our method and to demonstrate its effectiveness, we generated a relief (as a unique form of art creation) to narrate given temporal action scenes. A user study was conducted to experimentally compare the computer-selected poses with those selected by human participants. The results show that the proposed method can assist the selection of informative pose of character effectively

Data-driven synthesis of realistic human motion using motion graphs

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 53-56.Realistic human motions is an essential part of diverse range of media, such as feature films, video games and virtual environments. Motion capture provides realistic human motion data using sensor technology. However, motion capture data is not flexible. This drawback limits the utility of motion capture in practice. In this thesis, we propose a two-stage approach that makes the motion captured data reusable to synthesize new motions in real-time via motion graphs. Starting from a dataset of various motions, we construct a motion graph of similar motion segments and calculate the parameters, such as blending parameters, needed in the second stage. In the second stage, we synthesize a new human motion in realtime, depending on the blending techniques selected. Three different blending techniques, namely linear blending, cubic blending and anticipation-based blending, are provided to the user. In addition, motion clip preference approach, which is applied to the motion search algorithm, enable users to control the motion clip types in the result motion.Dirican, HüseyinM.S

A Temporally Coherent Neural Algorithm for Artistic Style Transfer

Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-by-frame animations. One such animation technique that has been widely used in the animation and visual effects industry is called rotoscoping and has allowed uniquely stylized animations to capture the motion of real life action sequences, however it is a very complex and time consuming process. Automating this arduous technique would free animators from performing frame by frame stylization and allow them to concentrate on their own artistic contributions. This thesis introduces a new artificial system based on an existing neural style transfer method which creates artistically stylized animations that simultaneously reproduce both the motion of the original videos that they are derived from and the unique style of a given artistic work. This system utilizes a convolutional neural network framework to extract a hierarchy of image features used for generating images that appear visually similar to a given artistic style while at the same time faithfully preserving temporal content. The use of optical flow allows the combination of style and content to be integrated directly with the apparent motion over frames of a video to produce smooth and visually appealing transitions. The implementation described in this thesis demonstrates how biologically-inspired systems such as convolutional neural networks are rapidly approaching human-level behavior in tasks that were once thought impossible for computers. Such a complex task elucidates the current and future technical and artistic capabilities of such biologically-inspired neural systems as their horizons expand exponentially. Further, this research provides unique insights into the way that humans perceive and utilize temporal information in everyday tasks. A secondary implementation that is explored in this thesis seeks to improve existing convolutional neural networks using a biological approach to the way these models adapt to their inputs. This implementation shows how these pattern recognition systems can be greatly improved by integrating recent neuroscience research into already biologically inspired systems. Such a novel hybrid activation function model replicates recent findings in the field of neuroscience and shows significant advantages over existing static activation functions

Dynamic Scene Creation from Text

Visual information is an integral part of our daily life. Typically, it tends to convey more information than simple textual information. A visual depiction of a textual story, as an animation or video, provides a more engaging and realistic experience and can be used in different applications. Examples of such applications include but are not limited to education, advertisement, crime scene investigation, forensic analysis of a crime, treatment of different types of mental and psychological disorders, etc. Manual 3D scene creation is a time-consuming process and requires expertise of individuals familiar with the content creation environment. Automatic scene generation using textual description and a library of developed components offers a quick and easy alternative for manual scene representation and proof of concept ideas. In this thesis, we propose a scheme for extraction of objects of interest and their spatial relationships from a user-provided textual description to create a 3D dynamic scene and animation to make it more realistic

VFX – A New Frontier: The Impact of Innovative Technology on Visual Effects

Although Visual Effects (VFX) are an increasingly important element of the media content demanded by audiences, of media production (filmmaking and storytelling) and of the media industries, VFX remains a relatively under-research area within academic media or film studies. Innovations in technology are instrumental to the continuous developments in VFX technology, enabling the evolution of storytelling techniques and expanding the boundaries of VFX content and the VFX industries. In particular, a new wave of cutting-edge technologies have contributed to a period of extensive technical and organisational changes in the VFX industry. The implementation of these technologies is occurring during a period of growth in demand for VFX content, ever hight standards of quality (in particular the realism of VFX effects) and resulting demand for VFX workers. Supplying this demand for both greater quantity and quality of VFX content has increased the pressure for VFX production to be as efficient as possible. This has brought pressure on production budgets (to produce more and better content from the same or even diminishing resources) and production timeframes (“turnaround times”). One result of all these changes is that VFX workers now confront a multitude of new challenges. This study investigates the new technology which is driving or enabling these changes and in particular focuses on the impact of implementing these technologies on VFX production (the VFX workflow). The study collects evidence to show how these new technologies, combined with the broader changes in the industry, are impacting VFX production and labour. The thesis approaches this research task by use economic and sociological theories of technology, innovation, and production/labour to provide a conceptual framework to use in understanding how these changes are impacting the products produced by the industry and the work experience of VFX professionals. The next step is to fill in the gaps in knowledge resulting from the relatively under-researched nature of VFX production withing academic media and film studies. The thesis provides a detailed account of the emergence and growth of “the VFX industry”, including historical and current product and process innovations. Rather than defining the object of study in relation to content genres or types of business, the study defines the industry in terms of workers using a common set of tools. This section of the thesis explores the economic and cultural causes of changes in the industry and maps out the qualitative changes in the creativity, job satisfaction and job security/precarity of VFX labour. The collection of primary data through interviews with industry professionals provides the unique contribution of this study, setting out how VFX work is changing in different content genres, types of business and production roles, at different hierarchical levels. This study contributes to the field by addressing the need for academic and empirical research in this neglected area of study. The thesis contributes original knowledge on the impact of current technological innovations by providing research based on primary data collected from interviews with the VFX workers impacted by the implementation of the technologies. Potential policy and practical applications of this research include assisting industry professionals in deconstructing the marketing “hype” around these cutting-edge technologies and outlining uncertainties and implications of these technologies, helping them in the complex decision making of evaluating and implementing current innovative technology