Abstract As the generation of realistic bas‐relief models from 2D images suffers from insufficient 3D depth information and severe under‐constraint, in this article, we propose a new framework for bas‐relief modeling based on 2D decorative images, which adopts conditional generative adversarial network to infer the normal information of the decorative bas‐reliefs from the greyscale information extracted from the images. For the variety of models, we extract the internal structure information through the saliency detection method based on scene perception, and use the transfer process based on the optimized texture synthesis algorithm to complete the normal editing from the source normal map to the new one, which can diversify and control the structure and detailed information of existed normal map. Finally, we adopt a bas‐relief reconstruction approach based on domain transfer recursive filter and surface from gradient to recover 2.5D information from predicted and transferred normal maps. Experiments on various model examples demonstrate the efficiency and diversity of the proposed method in reconstructing bas‐relief models from a single decorative image.

Abstract Visual feedback can help users understand the function, state, and outcome of a system during the pre‐, mid‐, and post‐interaction phases. Current visual feedback in 3D visualization scenarios takes less account of information transfer in terms of appearance design and dynamic behavior, which results in visual feedback being presented in a more engineered form and conveying simple information. For these issues, we proposed the concept and method of functional narrative animation as visual feedback for interactions in 3D visualization. We also provided a set of Unity‐based animation library and a plugin tool for configuring the animations. Finally, through user experiments and interviews, we analyzed the role of functional narrative animation as in the interactive visual feedback for 3D visualizations and make corresponding design recommendations.

We propose a crowd evacuation simulation method in the dynamic seismic environment, use real seismic data and the physical model to simulate motion phenomenon of movable objects, modify the flow field algorithm to guide individuals to avoid ground obstacles, and build an improved social force model to simulate crowd movement.

Abstract Motion deblurring is a challenging task in vision and graphics. Recent researches aim to deblur by using multiple sub‐networks with multi‐scale or multi‐patch inputs. However, scaling or splitting operations on input images inevitably loses the spatial details of the images. Meanwhile, their models are usually complex and computationally expensive. To address these problems, we propose a novel variant‐depth scheme. In particular, we utilize the multiple variant‐depth sub‐networks with scale‐invariant inputs to combine into a variant‐depth network (VDN). In our design, different levels of sub‐networks accomplish progressive deblurring effects without transforming the inputs, thereby effectively reducing the computational complexity of the model. Extensive experiments have shown that our VDN outperforms the state‐of‐the‐art motion deblurring methods while maintaining a lower computational cost. The source code is publicly available at: https://github.com/CaiGuoHS/VDN.

Abstract Virtual robots, including virtual animals, are expected to play a major role within affective and aesthetic interfaces, serious games, video instruction, and the personalization of educational instruction. Their actual impact, however, will very much depend on user perception of virtual characters as the uncanny valley hypothesis has shown that the design of virtual characters determines user experiences. In this article, we investigated whether the uncanny valley effect, which has already been found for the human‐like appearance of virtual characters, can also be found for animal‐like appearances. We conducted an online study (N = 163) in which six different animal designs were evaluated in terms of the following properties: familiarity, commonality, naturalness, attractiveness, interestingness, and animateness. The study participants differed in age (under 10–60 years) and origin (Europe, Asia, North America, and South America). For the evaluation of the results, we ranked the animal‐likeness of the character using both expert opinion and participant judgments. Next to that, we investigated the effect of movement and morbidity. The results confirm the existence of the uncanny valley effect for virtual animals, especially with respect to familiarity and commonality, for both still and moving images. The effect was particularly pronounced for morbid images. For naturalness and attractiveness, the effect was only present in the expert‐based ranking, but not in the participant‐based ranking. No uncanny valley effect was detected for interestingness and animateness. This investigation revealed that the appearance of virtual animals directly affects user perception and thus, presumably, impacts user experience when used in applied settings.

Abstract The importance of eyes for virtual characters stems from the intrinsic social cues in a person's eyes. While previous work on computer generated eyes has considered realism and naturalness, there has been little investigation into how details in the eye animation impact the perception of an avatar's internal emotional state. We present three large scale experiments (N≈500) that investigate the extent to which viewers can identify if an avatar is scared. We find that participants can identify a scared avatar with accuracy using cues in the eyes including pupil size variation, gaze, and blinks. Because eye trackers return pupil diameter in addition to gaze, our experiments inform practitioners that animating the pupil correctly will add expressiveness to a virtual avatar with negligible additional cost. These findings also have implications for creating expressive eyes in intelligent conversational agents and social robots.

This issue contains five papers. In the first paper, Kun Qian from King's College in London, UK, Meili Wang, from Northwest Agriculture and Forestry University, in Shannxi, China and Yaqing Cui, Bournemouth University, in Poole, UK propose a simulation ready model generation pipeline which can convert a non-manifold polygonal surface mesh into a degeneracy free surface while preserving the original model's surface parameterization attribute. Their pipeline includes two stages. The first stage is a voxelization and remesh based simulation ready model generation pipeline which can keep the shape of original 3D surface model meanwhile eliminate the non-manifold geometry. In the second stage, a cutting-based surface mesh parameterization transfer algorithm is proposed which can transfer the original surface parameterization to the simulation ready model. Detailed comparison with existing pipelines shows that the proposed pipeline can achieve surface parameterization preservation feature and is more suitable for improving the efficiency of virtual surgery production. In the second paper, Muhammad Usman and Petros Faloutsos, from York University in Toronto, Canada, Brandon Haworth, from University of Victoria in Canada and Mubbasir Kapadia, from Rutgers University, Piscataway in USA, propose a cross-browser service-based simulation analytics platform to analyze environment layouts with respect to occupancy and activity. Their platform allows users to access simulation services by uploading 3D environment models in numerous common formats, devise targeted simulation scenarios, run simulations, and instantly generate crowd-based analytics for their designs. The authors conducted a case study to showcase cross-domain applicability of their service-based platform, and a user study to evaluate the usability of this approach. In the third paper, Junxuan Bai, Rong Dai, Ju Dai, and Junjun Pan, from Beihang University in Beijing, and Peng Cheng Laboratory in Shenzhen, all in China propose a hybrid feature for emotional classification in dance performances. The hybrid feature is composed of an explicit feature and a deep feature. The explicit feature is calculated based on the Laban movement analysis, which considers the body, effort, shape, and space properties. The deep feature is obtained from latent representation through a 1D convolutional autoencoder. Eventually, the authors present an elaborate feature fusion network to attain the hybrid feature that is almost linearly separable. The abundant experiments demonstrate that their hybrid feature is superior to the separate features for the emotional classification in dance performances. The fourth paper, by Donya Ghafourzadeh, Sahel Fallahdoust, Cyrus Rahgoshay, André Beauchamp, Adeline Aubame, Eric Paquette, from Ecole de technologie supérieure, and Tiberiu Popa, from Concordia University, all in Montreal, Canada, present an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute editing workflow. They created a 3DMM by combining local part-based 3DMM for the eyes, nose, mouth, ears, and facial mask regions. Their local PCA-based approach uses a novel method to select the best eigenvectors from the local 3DMM to ensure that the combined 3DMM is expressive, while allowing accurate reconstruction. They provide different editing paradigms, all designed from the analysis of the data set. Their part-based 3DMM is compact, yet accurate, and compared to other 3DMM methods, it provides a new trade-off between local and global control. The results show that their part-based 3DMM approach has excellent generative properties and allows the user intuitive local control. The last paper by David Antonio Gomez Jauregui, from Ecole Supérieure des Technologies Industrielles Avancées, in Bidart, France, Tom Giraud, from University of Augsburg in Germany, Brice Isableu, from Aix-Marseille University, in Aix-en-Provence, France and Jean-Claude Martin, from LIMSI-CNRS, in Orsay, France propose a library of motion-captured movements that interviewers are most likely to display. They designed a fully automatic interactive virtual agent able to display these movements in response to the bodily movements of the user. Thirty-two participants presented themselves to this virtual agent during a simulated job interview. The authors explain the different hypotheses used (1) comparison between the performance of participants with human interviewers and the performance of participants with virtual interviewers, (2) comparison between mirror and random postural behaviors displayed by a female vs. a male virtual interviewer, (3) correlation between the participants' performance and their personality traits.

Abstract Humanoid navigation has been the center of attraction among robotic researchers since many years by virtue of their increasing use in industrial automation and smart manufacturing. In the current research, a firefly based computer vision integrated navigational analysis has been performed on humanoid robots for smooth movement by negotiation with obstacles present in complicated terrains. Here, the logic of the firefly algorithm has been used to design the controller by careful consideration of the navigational parameters. A computer vision based method is integrated along with the developed controller to resolve some conflicting situations that may arise by encountering large sized obstacle or detection of an obstacle exactly in front of the robot where the robot becomes confused regarding the direction of turn. The developed navigational model has been tested in simulation environments by considering NAO robot as the humanoid platform. The simulation results are also verified through an experimental platform developed under research laboratory conditions. The data obtained from both the platforms are compared in terms of navigational parameters and are found to be in close proximity to each other. Finally, the developed navigational model is also authenticated against other existing models for validating better efficiency.

We propose a framework that integrates reinforcement learning and blend-trees to generate animation of multiple agents for object transportation. The main idea is that in the learning stage, policies are learned to control agents to perform specific skills. The policies determine the blending parameters of the blend-trees to achieve locomotion control of the agents. In the simulation stage, the policies are combined to control the agents to navigate, push objects, and adjust orientation of the objects.

Abstract Virtual reality (VR) volleyball games can provide an immersive entertainment experience and facilitate users to get familiar with game rules. In real world, players often experience emotions from scores, strokes, passes, and the influence from their teammates or opponents, and so forth. However, seldom studies are carried out for this emotional experience in a virtual environment, as most VR volleyball games mainly concentrate on the game playing, such as body movements. In this article, we propose to enhance emotional experience by building emotional virtual characters in VR volleyball games. The virtual characters cannot only arouse their emotions but also express their facial expressions according to the game situation. Emotion patterns are learned from real‐world volleyball matches. The results demonstrate that our framework has great potential to enhance the user's emotional experience and engagement.