Characteristic Pose Research Articles

Cartoon copyright recognition method based on character personality action

Aiming at the problem of cartoon piracy and plagiarism, this paper proposes a method of cartoon copyright recognition based on character personality actions. This method can be used to compare the original cartoon actions with the action characteristics of pirated or copied cartoons to identify whether there is piracy or plagiarism. Firstly, an image preprocessing scheme for character extraction is designed. GrabCut interactive image segmentation algorithm was used to obtain cartoon characters, and then binarization and morphological processing were performed on the results. Secondly, a feature extraction scheme based on character profile, moving character and character pose is designed. By extracting the perimeter and area of the character contour, the length-to-width ratio of the smallest rectangle and the inclination angle of the contour, the character contour features are obtained. The three-dimensional coordinates are established by the central point position of the cartoon character in the two-dimensional image and the change of the character's zoom in and out, and the character's motion angle characteristics are calculated. By skeletonizing a character to obtain the pose characteristics, and using deburring operation to remove redundant branches, then extract the skeleton joint angle information. Finally, feature fusion is performed on the extracted features. The experimental results show that the proposed method breaks the limitation of the conventional single feature based recognition, and can better extract the character features including contour feature, motion feature and pose feature through multi-feature based extraction, so as to protect the cartoon copyright.

Sketch2Pose

Artists frequently capture character poses via raster sketches, then use these drawings as a reference while posing a 3D character in a specialized 3D software --- a time-consuming process, requiring specialized 3D training and mental effort. We tackle this challenge by proposing the first system for automatically inferring a 3D character pose from a single bitmap sketch, producing poses consistent with viewer expectations. Algorithmically interpreting bitmap sketches is challenging, as they contain significantly distorted proportions and foreshortening. We address this by predicting three key elements of a drawing, necessary to disambiguate the drawn poses: 2D bone tangents, self-contacts, and bone foreshortening. These elements are then leveraged in an optimization inferring the 3D character pose consistent with the artist's intent. Our optimization balances cues derived from artistic literature and perception research to compensate for distorted character proportions. We demonstrate a gallery of results on sketches of numerous styles. We validate our method via numerical evaluations, user studies, and comparisons to manually posed characters and previous work. Code and data for our paper are available at http://www-labs.iro.umontreal.ca/bmpix/sketch2pose/.

Learning‐based pose edition for efficient and interactive design

AbstractAuthoring an appealing animation for a virtual character is a challenging task. In computer‐aided keyframe animation artists define the key poses of a character by manipulating its underlying skeletons. To look plausible, a character pose must respect many ill‐defined constraints, and so the resulting realism greatly depends on the animator's skill and knowledge. Animation software provide tools to help in this matter, relying on various algorithms to automatically enforce some of these constraints. The increasing availability of motion capture data has raised interest in data‐driven approaches to pose design, with the potential of shifting more of the task of assessing realism from the artist to the computer, and to provide easier access to nonexperts. In this article, we propose such a method, relying on neural networks to automatically learn the constraints from the data. We describe an efficient tool for pose design, allowing Naïve users to intuitively manipulate a pose to create character animations.

Fast and deep facial deformations

Film-quality characters typically display highly complex and expressive facial deformation. The underlying rigs used to animate the deformations of a character's face are often computationally expensive, requiring high-end hardware to deform the mesh at interactive rates. In this paper, we present a method using convolutional neural networks for approximating the mesh deformations of characters' faces. For the models we tested, our approximation runs up to 17 times faster than the original facial rig while still maintaining a high level of fidelity to the original rig. We also propose an extension to the approximation for handling high-frequency deformations such as fine skin wrinkles. While the implementation of the original animation rig depends on an extensive set of proprietary libraries making it difficult to install outside of an in-house development environment, our fast approximation relies on the widely available and easily deployed TensorFlow libraries. In addition to allowing high frame rate evaluation on modest hardware and in a wide range of computing environments, the large speed increase also enables interactive inverse kinematics on the animation rig. We demonstrate our approach and its applicability through interactive character posing and real-time facial performance capture.

Interactive character posing with efficient collision handling

AbstractAn interactive interface for character posing is important in the field of computer graphics, games, and virtual reality. The inverse kinematics (IK) solver is the most popular approach that satisfies both the kinematic equations and the user‐defined position and orientation constraints on the end‐effectors. In this article, we present a novel interactive IK framework that efficiently handles various types of collisions and the spatial relationship constraints during the character posing process. Based on our method, a desired human pose can be easily obtained while resolving a collision with the environment and maintaining the spatial relationship of body parts. In addition, the character pose is smoothly updated throughout the user manipulation.

Last Kiss The Great Gatsby The Great Gatsby: An Edition of the Manuscript

Last Kiss The Great Gatsby The Great Gatsby: An Edition of the Manuscript

Outdoor Markerless Motion Capture with Sparse Handheld Video Cameras.

We present a method for outdoor markerless motion capture with sparse handheld video cameras. In the simplest setting, it only involves two mobile phone cameras following the character. This setup can maximize the flexibilities of data capture and broaden the applications of motion capture. To solve the character pose under such challenge settings, we exploit the generative motion capture methods and propose a novel model-view consistency that considers both foreground and background in the tracking stage. The background is modeled as a deformable 2D grid, which allows us to compute the background-view consistency for sparse moving cameras. The 3D character pose is tracked with a global-local optimization through minimizing our consistency cost. A novel motion regularizer is also proposed in the optimization to constrain the solution pose space. The whole process of the proposed method is simple as frame by frame video segmentation is not required. Our method outperforms several alternative methods on various examples demonstrated in the paper.

Learning Inverse Rig Mappings by Nonlinear Regression.

We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.

Shadow theatre

Shadow theatre is a genre of performance art in which the actors are only visible as shadows projected on the screen. The goal of this study is to generate animated characters, the shadows of which match a sequence of target silhouettes. This poses several challenges. The motion of multiple characters are carefully coordinated to form a target silhouette on the screen, and each character's pose should be stable, balanced, and plausible. The resulting character animation should be smooth and coherent spatially and temporally. We formulate the problem as nonlinear constrained optimization with objectives, which were designed to generate plausible human motions. Our optimization algorithm was primarily inspired by the heuristic strategies of professional shadow theatre actors. Their know-how was studied and then incorporated into our optimization formulation. We demonstrate the effectiveness of our approach with a variety of target silhouettes and 3D fabrication of the results.

Points‐based user interface for character posing

AbstractWe present a points‐based user interface for character posing. In our method, users insert a number of three‐dimensional (3D) points in a virtual environment. The system performs a linear search of a motion capture database for the best matched pose and then places the pose immediately in the virtual environment to be overlapped with the input points. For a fast and precise distance computation between the input points and the example poses from the database, we developed a closed‐form solution of the 3D points registration problem. To demonstrate the easiness and usability of our approach, we built a motion database including various kinds of human motion and conducted a user study of character posing tasks with non‐expert users. Copyright © 2016 John Wiley & Sons, Ltd.

Improving 3D Character Posing with a Gestural Interface.

The most time-consuming part of character animation is 3D character posing. Posing using a mouse is a slow and tedious task that involves sequences of selecting on-screen control handles and manipulating the handles to adjust character parameters, such as joint rotations and end effector positions. Thus, various 3D user interfaces have been proposed to make animating easier, but they typically provide less accuracy. The proposed interface combines a mouse with the Leap Motion device to provide 3D input. A usability study showed that users preferred the Leap Motion over a mouse as a 3D gestural input device. The Leap Motion drastically decreased the number of required operations and the task completion time, especially for novice users.

A Suggestive Interface for Sketch-based Character Posing

We present a user-friendly suggestive interface for sketch-based character posing. Our interface provides suggestive information on the sketching canvas in succession by combining image retrieval technique with 3D character posing, while the user is drawing. The system highlights the canvas region where the user should draw on and constrains the user's sketches in a reasonable solution space. This is based on an efficient image descriptor, which is used to measure the distance between the user's sketch and 2D views of 3D poses. In order to achieve faster query response, local sensitive hashing is involved in our system. In addition, sampling-based optimization algorithm is adopted to synthesize and optimize the retrieved 3D pose to match the user's sketches the best. Experiments show that our interface can provide smooth suggestive information to improve the reality of sketching poses and shorten the time required for 3D posing.

Character motion synthesis by principal component analysis and motion control interface by hands

AbstractIn this paper, we propose an interactive character motion control interface that uses hands. Using their hands and fingers, the user can control a large number of degrees of freedom at the same time. We applied principal component analysis to a set of sample poses and assigned the extracted principal components to each degree of freedom of the hands (such as the hand positions and finger bending/extending angles). The user can control the blending weights of the principal components and deform the character's pose by moving their hands and bending/extending their fingers. We introduced pose and action controls, so that we can alter the standing pose and perform various actions with deformations. So that various types of actions were possible, we constructed a number of action models in advance. We introduced action model selection and action execution mechanisms. We developed methods for computing the feature vector, for applying principal component analysis, and for pose and action synthesis. In addition, we introduced a pose transition method for performing a step motion when necessary to prevent foot sliding. We present our experimental results and demonstrate the effectiveness of our interface. Copyright © 2015 John Wiley & Sons, Ltd.

Human motion reconstruction from sparse 3D motion sensors using kernel CCA‐based regression

ABSTRACTThis paper presents a real‐time performance animation system that reproduces full‐body character animation based on sparse three‐dimensional (3D) motion sensors on a performer. Producing faithful character animation from this setting is a mathematically ill‐posed problem, because input data from the sensors are not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, we select similar poses from a motion database and build an online local model that transforms the low‐dimensional input signal into a high‐dimensional character pose. A regression method based on kernel canonical correlation analysis (CCA) is employed, because it effectively handles a wide variety of motions. Examples show that various human motions are naturally reproduced by the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

Authoring and animating painterly characters

Artists explore the visual style of animated characters through 2D concept art, since it affords them a nearly unlimited degree of creative freedom. Realizing the desired visual style, however, within the 3D character animation pipeline is often impossible, since artists must work within the technical limitations of the pipeline toolset. In order to expand the range of possible visual styles for digital characters, our research aims to incorporate the expressiveness afforded by 2D concept painting into the computer animation pipeline as a core component of character authoring and animation. While prior 3D painting methods focus on static geometry or simple animations, we develop tools for the more difficult task of character animation. Our system shows how 3D stroke-based paintings can be deformed using standard rigging tools. We also propose a configuration-space keyframing algorithm for authoring stroke effects that depend on scene variables such as character pose or light position. During animation, our system supports stroke-based temporal keyframing for one-off effects. Our primary technical contribution is a novel interpolation scheme for configuration-space keyframing that ensures smooth, controllable results. We demonstrate several characters authored with our system that exhibit painted effects difficult to achieve with traditional animation tools.

Continuous character control with low-dimensional embeddings

Interactive, task-guided character controllers must be agile and responsive to user input, while retaining the flexibility to be readily authored and modified by the designer. Central to a method's ease of use is its capacity to synthesize character motion for novel situations without requiring excessive data or programming effort. In this work, we present a technique that animates characters performing user-specified tasks by using a probabilistic motion model, which is trained on a small number of artist-provided animation clips. The method uses a low-dimensional space learned from the example motions to continuously control the character's pose to accomplish the desired task. By controlling the character through a reduced space, our method can discover new transitions, tractably precompute a control policy, and avoid low quality poses.

Animating Wrinkles by Example on Non-Skinned Cloth

The simulation of cloth with rich folds and wrinkles is a computationally expensive process. In this paper, we introduce an example-based algorithm for fast animation of plausible cloth wrinkles. Our algorithm does not depend on a character's pose, therefore it is valid for loose dresses, curtains, etc., not just cloth defined by skinning techniques. Central to our approach is a correspondence between low and high-resolution cloth deformations, both at the training and synthesis stages. Based on this correspondence, we define an algorithm for synthesizing cloth wrinkles as a function of the deformation of a low-resolution cloth and a set of example poses. We demonstrate the animation of plausible high-resolution wrinkles at high frame rates, suitable for interactive applications such as video games.

A Sketching Interface for Sitting Pose Design in the Virtual Environment

Character pose design is one of the most fundamental processes in computer graphics authoring. Although there are many research efforts in this field, most existing design tools consider only character body structure, rather than its interaction with the environment. This paper presents an intuitive sketching interface that allows the user to interactively place a 3D human character in a sitting position on a chair. Within our framework, the user sketches the target pose as a 2D stick figure and attaches the selected joints to the environment (e.g., the feet on the ground) with a pin tool. As reconstructing the 3D pose from a 2D stick figure is an ill-posed problem due to many possible solutions, the key idea in our paper is to reduce solution space by considering the interaction between the character and environment and adding physics constraints, such as balance and collision. Further, we formulated this reconstruction into a nonlinear optimization problem and solved it via the genetic algorithm (GA) and the quasi-Newton solver. With the GPU implementation, our system is able to generate the physically correct and visually pleasing pose at an interactive speed. The promising experimental results and user study demonstrates the efficacy of our method.

Informed character pose and proportion design

The use of pose and proportion to represent character traits is well established in art and psychology literature. However, there are no golden rules that quantify a generic design template for stylized character figure drawing. Given the wide variety of drawing styles and a large feature dimension space, it is a significant challenge to extract this information automatically from existing cartoon art. This paper outlines a game-inspired methodology for systematically collecting layman perception feedback, given a set of carefully chosen trait labels and character silhouette images. The rated labels were clustered and then mapped to the pose and proportion parameters of characters in the dataset. The trained model can be used to classify new drawings, providing valuable insight to artists who want to experiment with different poses and proportions in the draft stage. The proposed methodology was implemented as follows: (1) Over 200 full-body, front-facing character images were manually annotated to calculate pose and proportion; (2) A simplified silhouette was generated from the annotations to avoid copyright infringements and prevent users from identifying the source of our experimental figures; (3) An online casual role-playing puzzle game was developed to let players choose meaningful tags (role, physicality, and personality) for characters, where tags and silhouettes received equitable exposure; (4) Analysis on the generated data was done both in stereotype label space as well as character shape space; (5) Label filtering and clustering enabled dimension reduction of the large description space, and subsequently, a select set of design features were mapped to these clusters to train a neural network classifier; (6) Bayesian graphs were also mined to allow informed tweaks to the input feature set, in order to push the character toward a different label stereotype class. The mapping between the collected perception and shape data give us quantitative and qualitative insight into character design. It opens up applications for creative reuse of (and deviation from) existing character designs.

Motion synthesis and editing in low‐dimensional spaces

AbstractHuman motion is difficult to create and manipulate because of the high dimensionality and spatiotemporal nature of human motion data. Recently, the use of large collections of captured motion data has added increased realism in character animation. In order to make the synthesis and analysis of motion data tractable, we present a low‐dimensional motion space in which high‐dimensional human motion can be effectively visualized, synthesized, edited, parameterized, and interpolated in both spatial and temporal domains. Our system allows users to create and edit the motion of animated characters in several ways: The user can sketch and edit a curve on low‐dimensional motion space, directly manipulate the character's pose in three‐dimensional object space, or specify key poses to create in‐between motions. Copyright © 2006 John Wiley & Sons, Ltd.