Abstract We propose a new method to obtain the representative colors and their distributions of an image. Our intuition is that it is possible to derive the global model from the local distributions. Beginning by sampling pure colors, we build a hierarchical representation of colors in the image via a bottom‐up approach. From the resulting hierarchy, we can obtain satisfactory palettes/color models automatically without a predefined size. Furthermore, we provide interactive operations to manipulate the results which allow the users to reflect their intention directly. In our experiment, we show that the proposed method produces more succinct results that faithfully represent all the colors in the image with an appropriate number of components. We also show that the proposed interactive approach can improve the results of applications such as recoloring and soft segmentation.

The perception of objects, depth, and distance has been repeatedly shown to be divergent between virtual and physical environments. We hypothesize that many of these discrepancies stem from incorrect geometric viewing parameters, specifically that physical measurements of eye position are insufficiently precise to provide proper viewing parameters. In this paper, we introduce a perceptual calibration procedure derived from geometric models. While most research has used geometric models to predict perceptual errors, we instead use these models inversely to determine perceptually correct viewing parameters. We study the advantages of these new psychophysically determined viewing parameters compared to the commonly used measured viewing parameters in an experiment with 20 subjects. The perceptually calibrated viewing parameters for the subjects generally produced new virtual eye positions that were wider and deeper than standard practices would estimate. Our study shows that perceptually calibrated viewing parameters can significantly improve depth acuity, distance estimation, and the perception of shape.

Externally observing the experience of a participant in a virtual environment is generally accomplished by viewing an egocentric perspective. Monitoring this view can often be difficult for others to watch due to unwanted camera motions that appear unnatural and unmotivated. We present a novel method for reducing the unnaturalness of these camera motions by minimizing camera movement while maintaining the context of the participant's observations. For each time-step, we compare the parts of the scene viewed by the virtual participant to the parts of the scene viewed by the camera. Based on the similarity of these two viewpoints we next determine how the camera should be adjusted. We present two means of adjustment, one which continuously adjusts the camera and a second which attempts to stop camera movement when possible. Empirical evaluation shows that our method can produce paths that have substantially shorter travel distances, are easier to watch and maintain the original observations of the participant's virtual experience.

ABSTRACT We propose a new image‐space technique to summarize a 3D animation sequence into a single image by using the depth information of the animation. The proposed method extracts important frames from the animation sequence, where the important frames are representative of the sequence and keep the complexity of a composed image as simple as possible. Assuming that the input sequence consists of a set of images with depth information, we construct a composite depth image and its gradient image. We evaluate the importance of each frame by its amount of contribution to the gradient of the composite depth image. The frames of higher importance are located more likely at which the motion of a moving object reaches the extreme positions, the fastest speed, and the slowest speed in image space. From the most important frames to the least ones, we recursively compose the important frames into a single composite image while keeping the complexity of the composite image by evaluating the amount of self‐overlap. The threshold value for the amount of overlap allows a user to control interactively the visual complexity of the composed image. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract In this paper, we propose a technique to produce artistic strokes in a variety of drawing material based on example images. Our approach is to divide example strokes scanned from images into small pieces along their stroke directions and synthesize a novel stroke by rearranging them along a user specified curve. The visible quality of a synthesized stroke can be maintained by utilizing the connectivity information stored in a directed graph constructed in the preprocessing step. At run‐time, the graph is traversed to find a path best matching the user specification given as a curve and additional information. The results of our experiments shows that visually convincing strokes of various materials can be generated efficiently.

Abstract In this paper, we introduce a novel framework that allows users to synthesize the expression of a 3D character by providing a intuitive set of parametric controls. Assuming that human face movements are formed by a set of basis actuation, we analyze a set of real expressions to extract this set together with skin deformation due to the actuation of face. To do this, we first decompose the movement of each marker into a set of distinctive movements. Independent component analysis technique is then adopted to find a independent set of actuations. Our simple and efficient skin deformation model are learned to reproduce the realistic movements of facial parts due to the actuations. In this framework, users can animate characters' faces by controlling the amount actuation or by directly manipulating the face geometry. In addition, the proposed method can be applied to expression transfer which reproduces one character's expression in another character's face. Experimental results demonstrate that our method can produce realistic expression efficiently.

Abstract Human 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.

Abstract In this paper, we present an integrated framework of on‐line motion blending for locomotion generation. We first provide a novel scheme for incremental timewarping, which always guarantees that the time goes forward. Combining the idea of motion blending with that of posture rearrangement, we introduce a motion transition graph to address on‐line motion blending and transition simultaneously. Guided by a stream of motion specifications, our motion synthesis scheme moves from node to node in an on‐line manner while blending a motion at a node and generating a transition motion at an edge. For smooth on‐line motion transition, we also attach a set of example transition motions to an edge. To represent similar postures consistently, we exploit the inter‐frame coherency embedded in the input motion specification. Finally, we provide a comprehensive solution to on‐line motion retargeting by integrating existing techniques. Copyright © 2004 John Wiley & Sons, Ltd.

Many popular motion editing methods do not take physical principles into account potentially producing implausible motions. This paper introduces an efficient method for touching up edited motions to improve physical plausibility. We start by estimating a mass distribution consistent with reference motions known to be physically correct. The edited motion is then divided into ground and flight stages and adjusted to enforce appropriate physical laws, for, respectively, zero moment point (ZMP) constraints and correct ballistic trajectory. Unlike previous methods, we do not solve a nonlinear optimization to calculate the adjustment. Instead, closed-form methods are used to construct a hierarchical displacement map which sequentially refines user-specified degrees of freedom at different scales. This is combined with standard methods for kinematic constraint enforcement, yielding an efficient and scalable editing method that allows users to model real human behaviors. The potential of our approach is demonstrated in a number of examples.

Many virtual environments and games must be populated with synthetic characters to create the desired experience. These characters must move with sufficient realism, so as not to destroy the visual quality of the experience, yet be responsive, controllable, and efficient to simulate. In this paper we present an approach to character motion called Snap-Together Motion that addresses the unique demands of virtual environments. Snap-Together Motion (STM) preprocesses a corpus of motion capture examples into a set of short clips that can be concatenated to make continuous streams of motion. The result process is a simple graph structure that facilitates efficient planning of character motions. A user-guided process selects "common" character poses and the system automatically synthesizes multi-way transitions that connect through these poses. In this manner well-connected graphs can be constructed to suit a particular application, allowing for practical interactive control without the effort of manually specifying all transitions.

No abstract available.

Computer puppetry maps the movements of a performer to an animated character in real-time. In this article, we provide a comprehensive solution to the problem of transferring the observations of the motion capture sensors to an animated character whose size and proportion may be different from the performer's. Our goal is to map as many of the important aspects of the motion to the target character as possible, while meeting the online, real-time demands of computer puppetry. We adopt a Kalman filter scheme that addresses motion capture noise issues in this setting. We provide the notion of dynamic importance of an end-effector that allows us to determine what aspects of the performance must be kept in the resulting motion. We introduce a novel inverse kinematics solver that realizes these important aspects within tight real-time constraints. Our approach is demonstrated by its application to broadcast television performances.