Collecting and evaluating the CUNY ASL corpus for research on American Sign Language animation

While there is great potential for sign language animation generation software to improve the accessibility of information for deaf individuals with low written-language literacy, the understandability of current sign language animation systems is limited. Data-driven methodologies using annotated sign language corpora encoding detailed human movement have enabled some researchers to address several key linguistic challenges in ASL generation. This article motivates and describes our current research on collecting a motion-capture corpus of American Sign Language (ASL). As an evaluation of our motion-capture configuration, calibration, and recording protocol, we have conducted several rounds of evaluation studies with native ASL signers, and we have made use of our collected data to synthesize novel animations of ASL, which have also been evaluated in experimental studies with native signers.     

Comprehending object and process models: an empirical study

We report the results of an empirical study comparing user comprehension of object oriented (OO) and process oriented (PO) models. The fundamental difference is that while OO models tend to focus on structure, PO models tend to emphasize behaviour or processes. Proponents of the OO modeling approach argue that it lends itself naturally to the way humans think. However, evidence from research in cognitive psychology and human factors suggests that human problem solving is innately procedural. Given these conflicting viewpoints, we investigate empirically if OO models are in fact easier to understand than PO models. But, as suggested by the theory of cognitive fit, model comprehension may be influenced by task-specific characteristics. We therefore compare OO and PO models based on whether the comprehension activity involves: 1) only structural aspects, 2) only behavioral aspects, or 3) a combination of structural and behavioral aspects. We measure comprehension through subjects' responses to questions designed along these three dimensions. Results show that for most of the simple questions, no significant difference was observed insofar as model comprehension is concerned. For most of the complex questions, however, the PO model was found to be easier to understand than the OO model. In addition to describing the process and the outcomes of the experiments, we present the experimental method employed as a viable approach for conducting research into various phenomena related to the efficacy of alternative systems analysis and design methods. We also identify areas where future research is necessary, along with a recommendation of appropriate research methods for empirical examination     

Quantitative and Qualitative Analysis of the Perception of Semi‐Transparent Structures in Direct Volume Rendering

Direct Volume Rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. With DVR semi‐transparency is facilitated to convey the complexity of the data. Unfortunately, semi‐transparency introduces challenges in spatial comprehension of the data, as the ambiguities inherent to semi‐transparent representations affect spatial comprehension. Accordingly, many techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we present our findings obtained from two evaluations investigating the perception of semi‐transparent structures from volume rendered images. We have conducted a user evaluation in which we have compared standard DVR with five techniques previously proposed to enhance the spatial comprehension of DVR images. In this study, we investigated the perceptual performance of these techniques and have compared them against each other in a large‐scale quantitative user study with 300 participants. Each participant completed micro‐tasks designed such that the aggregated feedback gives insight on how well these techniques aid the user to perceive depth and shape of objects. To further clarify the findings, we conducted a qualitative evaluation in which we interviewed three experienced visualization researchers, in order to find out if we can identify the benefits and shortcomings of the individual techniques.     

Evaluation of illustration‐inspired techniques for time‐varying data visualization

Illustration‐inspired techniques have provided alternative ways to visualize time‐varying data. Techniques such as speedlines, flow ribbons, strobe silhouettes and opacity‐based techniques provide temporal context to the current timestep being visualized. We evaluated the effectiveness of these illustrative techniques by conducting a user study. We compared the ability of subjects to visually track features using snapshots, snapshots augmented by illustration techniques, animations, and animations augmented by illustration techniques. User accuracy, time required to perform a task, and user confidence were used as measures to evaluate the techniques. The results indicate that the use of illustration‐inspired techniques provides a significant improvement in user accuracy and the time required to complete the task. Subjects performed significantly better on each metric when using augmented animations as compared to augmented snapshots.     

An approach to effortless construction of program animations

Program animation systems have not been as widely adopted by computer science educators as we might expect from the firm belief that they can help in enhancing computer science education. One of the most notable obstacles to their adoption is the considerable effort that the production of program animations represents for the instructor. We present here an approach to reduce such a workload based on the automatic generation of visualizations and animations. The user may customize them in a user-friendly way to construct more expressive program animations. These operations are carried out by means of a user-friendly manipulation based on the metaphor of office documents. We have applied this approach to the functional paradigm by extending the WinHIPE programming environment. Finally, we report on the successful results of an evaluation performed to measure its ease of use.     

Crowd sculpting: A space‐time sculpting method for populating virtual environments

We introduce “Crowd Sculpting”: a method to interactively design populated environments by using intuitive deformation gestures to drive both the spatial coverage and the temporal sequencing of a crowd motion. Our approach assembles large environments from sets of spatial elements which contain inter‐connectible, periodic crowd animations. Such a “Crowd Patches” approach allows us to avoid expensive and difficult‐to‐control simulations. It also overcomes the limitations of motion editing, that would result into animations delimited in space and time. Our novel methods allows the user to control the crowd patches layout in ways inspired by elastic shape sculpting: the user creates and tunes the desired populated environment through stretching, bending, cutting and merging gestures, applied either in space or time. Our examples demonstrate that our method allows the space‐time editing of very large populations and results into endless animation, while offering real‐time, intuitive control and maintaining animation quality.     

Effects of spatial ability and richness of motion cue on learning in mechanically complex domain

This study seeks to examine the impact of individual differences in the spatial ability of learners to integrate verbal information and three modes of visual representations. Several hypotheses were tested, including that (1) individual difference in spatial ability should influence the learning of theoretical knowledge when the instructional materials present a static visual representation at the lowest motion cue richness, and (2) both animations and the static visual representation containing motion cues should be more effective than static visual representation, especially for learners with low spatial ability. In the experiment, 60 learners were classified as having either low or high spatial ability on the basis of their performance on the Kit of Factor Referenced Cognitive Tests. The learners got knowledge from written explanations describing a four-stroke engine mechanism in a computer-based format. Also, written explanations were reinforced by corresponding visual representations with three levels of motion cue richness (static images, static images with motion cues or animations). Understanding was measured by a problem-solving transfer test. The results indicate that (1) presenting written explanations with corresponding animations did not improve performance of the learners with high spatial ability, (2) for the learners with low spatial ability, learning was enhanced by the use of animations, (3) merely adding motion cues to the static visual representation did not improve learning of the learners with low spatial ability, and (4) use of animations did not help learners with low spatial ability more than those with high spatial ability.     

Self adaptive animation based on user perspective

In this paper we present a new character animation technique in which the animation adapts itself based on the change in the user’s perspective, so that when the user moves and their point of viewing the animation changes, then the character animation adapts itself in response to that change. The resulting animation, generated in real-time, is a blend of key animations provided a priori by the animator. The blending is done with the help of efficient dual-quaternion transformation blending. The user’s point of view is tracked using either computer vision techniques or a simple user-controlled input modality, such as mouse-based input. This tracked point of view is then used to suitably select the blend of animations. We show a way to author and use such animations in both virtual as well as augmented reality scenarios and demonstrate that it significantly heightens the sense of presence for the users when they interact with such self adaptive animations of virtual characters.     

Full immersive virtual environment CAVE in chemistry education

By comparing two-dimensional (2D) chemical animations designed for computer’s desktop with three-dimensional (3D) chemical animations designed for the full immersive virtual reality environment CAVE^TM we studied how virtual reality environments could raise student’s interest and motivation for learning. By using the 3ds max^TM, we can visualize the chemical phenomena easily and quickly without knowing any special computer language and export the application to files which are compatible with the CAVE^TM (Object or OpenGL files). After the participation in 3D animations at the CAVE^TM students comprehended the molecules’ structure and their changes during a chemical reaction better than during the 2D animations on the computer’s desktop, as the limitations of human vision had been overcome. Furthermore, the students were enthusiastic, as they had the feeling that they were inside the chemical reactions and they were facing the 3D molecules as if they were real objects front of them.     

The effect of animation on comprehension and interest

Abstract  Although animations are believed to be effective in learning and teaching, several studies have failed to confirm this. Nevertheless, animations might be more attractive and motivating. Fourth and sixth grade students learned the operation of a bicycle pump from graphics that were: (i) presented simultaneously; (ii) presented successively; (iii) self-paced, or (iv) animated. The presentation mode affected evaluation of perceived comprehensibility, interestingness, enjoyment and motivation, but not comprehension test score. Fourth graders who were low in need for cognition rated the animations as more enjoyable and motivating, whereas sixth graders rated self-paced graphics as more interesting and motivating. The evaluations of sixth graders correspond to results of many studies on learning. Animations are not more effective than equivalent static graphics in learning, and they are not seen as more motivating by sixth graders.     

Simulation of Dendritic Painting

We present a new system for interactive dendritic painting. Dendritic painting is characterized by the unique and intricate branching patterns that grow from the interaction of inks, solvents and medium. Painting sessions thus become very dynamic and experimental. To achieve a compelling simulation of this painting technique we introduce a new Reaction-Diffusion model with carefully designed terms to allow natural interactions in a painting context. We include additional user control not possible in the real world to guide and constrain the growth of the patterns in expressive ways. Our multi-field model is able to capture and simulate all these complex phenomena efficiently in real time, expanding the tools available to the digital artist, while producing compelling animations for motion graphics.     

Teaching genetics with multimedia results in better acquisition of knowledge and improvement in comprehension

The main goal of this study was to explore whether the use of multimedia in genetics instruction contributes more to students' knowledge and comprehension than other instructional modes. We were also concerned with the influence of different instructional modes on the retention of knowledge and comprehension. In a quasi‐experimental design, four comparable groups of 3rd and 4th grade high school students were taught the process of protein synthesis: group 1 was taught in the traditional lecture format (n = 112 students), group 2 only by reading text (n = 124 students), group 3 through multimedia that integrated two short computer animations (n = 115 students) and group 4 by text supplemented with illustrations (n = 117 students). All students received one pre‐test in order to estimate their prior knowledge, and two post‐tests in order to assess knowledge and comprehension immediately after learning and again after 5 weeks. Results showed that students comprising groups 3 and 4 acquired better knowledge and improved comprehension skills than the other two groups. Similar results were observed for retention of acquired knowledge and improved comprehension. These findings lead to the conclusion that better learning outcomes can be obtained by the use of animations or at least illustrations when learning genetics.     

Identifying an optimal analysis level in multiscalar regionalization: A study case of social distress in Greater Santiago

Assembling spatial units into meaningful clusters is a challenging task, as it must cope with a consequential computational complexity while controlling for the modifiable areal unit problem (MAUP), spatial autocorrelation and attribute multicolinearity. Nevertheless, these effects can reveal significant interactions among diverse spatial phenomena, such as segregation and economic specialization. Various regionalization methods have been developed in order to address these questions, but key fundamental properties of the aggregation of spatial entities are still poorly understood. In particular, due to the lack of an objective stopping rule, the question of determining an optimal number of clusters is yet unresolved. Therefore, we develop a clustering algorithm which is sensitive to scalar variations of multivariate spatial correlations, recalculating PCA scores at several aggregation steps in order to account for differences in the span of autocorrelation effects for diverse variables. With these settings, the scalar evolution of correlation, compactness and isolation measures is compared between empirical and 120 random datasets, using two dissimilarity measures. Remarkably, adjusting several indicators with real and simulated data allows for a clear definition of a stopping rule for spatial hierarchical clustering. Indeed, increasing correlations with scale in random datasets are spurious MAUP effects, so they can be discounted from real data results in order to identify an optimal clustering level, as defined by the maximum of authentic spatial self-organization. This allows singling out the most socially distressed areas in Greater Santiago, thus providing relevant socio-spatial insights from their cartographic and statistical analysis. In sum, we develop a useful methodology to improve the fundamental comprehension of spatial interdependence and multiscalar self-organizing phenomena, while linking these questions to relevant real world issues.     

Top-down and bottom-up influences on learning from animations

To evaluate how top-down and bottom-up processes contribute to learning from animated displays, we conducted four experiments that varied either in the design of animations or the prior knowledge of the learners. Experiments 1–3 examined whether adding interactivity and signaling to an animation benefits learners in developing a mental model of a mechanical system. Although learners utilized interactive controls and signaling devices, their comprehension of the system was no better than that of learners who saw animations without these design features. Furthermore, the majority of participants developed a mental model of the system that was incorrect and inconsistent with information displayed in the animation. Experiment 4 tested effects of domain knowledge and found, surprisingly, that even some learners with high domain knowledge initially constructed the incorrect mental model. After multiple exposures to the materials, the high knowledge learners revised their mental models to the correct one, while the low-knowledge learners maintained their erroneous models. These results suggest that learning from animations involves a complex interplay between top-down and bottom-up processes and that more emphasis should be placed on how prior knowledge is applied to interpreting animations.     

Glances,glares, and glowering: how should a virtual human express emotion through gaze?

Gaze is an extremely powerful expressive signal that is used for many purposes, from expressing emotion to regulating human interaction. The use of gaze as a signal has been exploited to strong effect in hand-animated characters, greatly enhancing the believability of the character’s simulated life. However, virtual humans animated in real-time have been less successful at using expressive gaze. One reason for this is that we lack a model of expressive gaze in virtual humans. A gaze shift towards any specific target can be performed in many different ways, using many different expressive manners of gaze, each of which can potentially imply a different emotional or cognitive internal state. However, there is currently no mapping that describes how a user will attribute these internal states to a virtual character performing a gaze shift in a particular manner. In this paper, we begin to address this by providing the results of an empirical study that explores the mapping between an observer’s attribution of emotional state to gaze. The purpose of this mapping is to allow for an interactive virtual human to generate believable gaze shifts that a user will attribute a desired emotional state to. We have generated a set of animations by composing low-level gaze attributes culled from the nonverbal behavior literature. Then, subjects judged the animations displaying these attributes. While the results do not provide a complete mapping between gaze and emotion, they do provide a basis for a generative model of expressive gaze.     

3D动画自动生成的学习系统

手机3D动画自动生成系统实现了在计算机辅助的条件下短信动画的全自动生成.本文为手机3D动画自动生成系统设计并实现一个学习系统,该学习系统利用随机森林算法生成一个以用户评价为分类结果的学习模型去指导手机3D动画自动生成系统生成更让用户满意的动画,同时学习系统能够随着手机3D动画自动生成系统的运转不断更新学习模型,最终实现“不断学习”的学习能力.     

Deep Deformation Detail Synthesis for Thin Shell Models

In physics-based cloth animation, rich folds and detailed wrinkles are achieved at the cost of expensive computational resources and huge labor tuning. Data-driven techniques make efforts to reduce the computation significantly by utilizing a preprocessed database. One type of methods relies on human poses to synthesize fitted garments, but these methods cannot be applied to general cloth animations. Another type of methods adds details to the coarse meshes obtained through simulation, which does not have such restrictions. However, existing works usually utilize coordinate-based representations which cannot cope with large-scale deformation, and requires dense vertex correspondences between coarse and fine meshes. Moreover, as such methods only add details, they require coarse meshes to be sufficiently close to fine meshes, which can be either impossible, or require unrealistic constraints to be applied when generating fine meshes. To address these challenges, we develop a temporally and spatially as-consistent-as-possible deformation representation (named TS-ACAP) and design a DeformTransformer network to learn the mapping from low-resolution meshes to ones with fine details. This TS-ACAP representation is designed to ensure both spatial and temporal consistency for sequential large-scale deformations from cloth animations. With this TS-ACAP representation, our DeformTransformer network first utilizes two mesh-based encoders to extract the coarse and fine features using shared convolutional kernels, respectively. To transduct the coarse features to the fine ones, we leverage the spatial and temporal Transformer network that consists of vertex-level and frame-level attention mechanisms to ensure detail enhancement and temporal coherence of the prediction. Experimental results show that our method is able to produce reliable and realistic animations in various datasets at high frame rates with superior detail synthesis abilities compared to existing methods.     

Generating surface crack patterns

We present a method for generating surface crack patterns that appear in materials such as mud, ceramic glaze, and glass. To model these phenomena, we build upon existing physically based methods. Our algorithm generates cracks from a stress field defined heuristically over a triangle discretization of the surface. The simulation produces cracks by evolving this field over time. The user can control the characteristics and appearance of the cracks using a set of simple parameters. By changing these parameters, we have generated examples similar to a variety of crack patterns found in the real world. We assess the realism of our results by comparison with photographs of real-world examples. Using a physically based approach also enables us to generate animations similar to time-lapse photography.