Lip syncing method for realistic expressive 3D face model

Lip synchronization of 3D face model is now being used in a multitude of important fields. It brings a more human, social and dramatic reality to computer games, films and interactive multimedia, and is growing in use and importance. High level of realism can be used in demanding applications such as computer games and cinema. Authoring lip syncing with complex and subtle expressions is still difficult and fraught with problems in terms of realism. This research proposed a lip syncing method of realistic expressive 3D face model. Animated lips requires a 3D face model capable of representing the myriad shapes the human face experiences during speech and a method to produce the correct lip shape at the correct time. The paper presented a 3D face model designed to support lip syncing that align with input audio file. It deforms using Raised Cosine Deformation (RCD) function that is grafted onto the input facial geometry. The face model was based on MPEG-4 Facial Animation (FA) Standard. This paper proposed a method to animate the 3D face model over time to create animated lip syncing using a canonical set of visemes for all pairwise combinations of a reduced phoneme set called ProPhone. The proposed research integrated emotions by the consideration of Ekman model and Plutchik’s wheel with emotive eye movements by implementing Emotional Eye Movements Markup Language (EEMML) to produce realistic 3D face model. © 2017 Springer Science+Business Media New Yor

Drawing from motion capture : developing visual languages of animation

The work presented in this thesis aims to explore novel approaches of combining motion capture with drawing and 3D animation. As the art form of animation matures, possibilities of hybrid techniques become more feasible, and crosses between traditional and digital media provide new opportunities for artistic expression. 3D computer animation is used for its keyframing and rendering advancements, that result in complex pipelines where different areas of technical and artistic specialists contribute to the end result. Motion capture is mostly used for realistic animation, more often than not for live-action filmmaking, as a visual effect. Realistic animated films depend on retargeting techniques, designed to preserve actors performances with a high degree of accuracy. In this thesis, we investigate alternative production methods that do not depend on retargeting, and provide animators with greater options for experimentation and expressivity. As motion capture data is a great source for naturalistic movements, we aim to combine it with interactive methods such as digital sculpting and 3D drawing. As drawing is predominately used in preproduction, in both the case of realistic animation and visual effects, we embed it instead to alternative production methods, where artists can benefit from improvisation and expression, while emerging in a three-dimensional environment. Additionally, we apply these alternative methods for the visual development of animation, where they become relevant for the creation of specific visual languages that can be used to articulate concrete ideas for storytelling in animation

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Efficient Human Pose Estimation with Image-dependent Interactions

Human pose estimation from 2D images is one of the most challenging and computationally-demanding problems in computer vision. Standard models such as Pictorial Structures consider interactions between kinematically connected joints or limbs, leading to inference cost that is quadratic in the number of pixels. As a result, researchers and practitioners have restricted themselves to simple models which only measure the quality of limb-pair possibilities by their 2D geometric plausibility. In this talk, we propose novel methods which allow for efficient inference in richer models with data-dependent interactions. First, we introduce structured prediction cascades, a structured analog of binary cascaded classifiers, which learn to focus computational effort where it is needed, filtering out many states cheaply while ensuring the correct output is unfiltered. Second, we propose a way to decompose models of human pose with cyclic dependencies into a collection of tree models, and provide novel methods to impose model agreement. Finally, we develop a local linear approach that learns bases centered around modes in the training data, giving us image-dependent local models which are fast and accurate. These techniques allow for sparse and efficient inference on the order of minutes or seconds per image. As a result, we can afford to model pairwise interaction potentials much more richly with data-dependent features such as contour continuity, segmentation alignment, color consistency, optical flow and multiple modes. We show empirically that these richer models are worthwhile, obtaining significantly more accurate pose estimation on popular datasets

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Mesh modification using deformation gradients

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 117-131).Computer-generated character animation, where human or anthropomorphic characters are animated to tell a story, holds tremendous potential to enrich education, human communication, perception, and entertainment. However, current animation procedures rely on a time consuming and difficult process that requires both artistic talent and technical expertise. Despite the tremendous amount of artistry, skill, and time dedicated to the animation process, there are few techniques to help with reuse. Although individual aspects of animation are well explored, there is little work that extends beyond the boundaries of any one area. As a consequence, the same procedure must be followed for each new character without the opportunity to generalize or reuse technical components. This dissertation describes techniques that ease the animation process by offering opportunities for reuse and a more intuitive animation formulation. A differential specification of arbitrary deformation provides a general representation for adapting deformation to different shapes, computing semantic correspondence between two shapes, and extrapolating natural deformation from a finite set of examples.(cont.) Deformation transfer adds a general-purpose reuse mechanism to the animation pipeline by transferring any deformation of a source triangle mesh onto a different target mesh. The transfer system uses a correspondence algorithm to build a discrete many-to-many mapping between the source and target triangles that permits transfer between meshes of different topology. Results demonstrate retargeting both kinematic poses and non-rigid deformations, as well as transfer between characters of different topological and anatomical structure. Mesh-based inverse kinematics extends the idea of traditional skeleton-based inverse kinematics to meshes by allowing the user to pose a mesh via direct manipulation. The user indicates the dass of meaningful deformations by supplying examples that can be created automatically with deformation transfer, sculpted, scanned, or produced by any other means. This technique is distinguished from traditional animation methods since it avoids the expensive character setup stage. It is distinguished from existing mesh editing algorithms since the user retains the freedom to specify the class of meaningful deformations. Results demonstrate an intuitive interface for posing meshes that requires only a small amount of user effort.by Robert Walker Sumner.Ph.D