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This is a case study focused on the development of LOVOT , a consumer home robot that went on sale in 2019. Since its unveiling in 2018, approximately 20,000 people have visited the LOVOT demonstration event as of the end of July 2019. In practical terms, LOVOT lives at home with the user long-term, similar to a companion animal; LOVOT performs continuous natural motion for user comfort. We applied movements using professional animator techniques based on the principles of traditional animation to LOVOT . We have identified specific practical techniques in home robot animation through iterative prototyping in our synergetic development of character animation techniques and tools. In this paper, we introduce our practical methods to develop many behaviors in a single robotic agent and is the collaborative efforts of a group of people with diverse professional backgrounds. Furthermore, we summarize how traditional techniques of character animation were applied, and new techniques were required from the perspective of professional animators.

Animation techniques have been completely transformed by the union of Artificial Intelligence (AI) and biomechanical modeling, particularly in 2D animation. This study looks at a combination of AI and biomechanics to address the challenges of simulating 2D animation. Current approaches in 2D animation often struggle to achieve lifelike and fluid movements, especially when representing complex motion or interaction. These traditional techniques rely on manual keyframing or physics simulation, which may be time-consuming and do not provide the rich detail needed for realism in animations. To meet these aspects, this study suggested 2D animation using Artificial Intelligence with Biomechanical Modeling (2D-AI-BM). Our approach thus harnesses Deep Neural Network (DNN) for moving forecasts and improvement using biopsychological principles to help us imitate natural human actions better. In addition to character animation, it could apply to interactive storytelling and educational simulations. As a result, animators get more control over motion generation while drastically reducing the necessity for manual intervention through this fusion of AI and biomechanics, which smoothens the production pipeline for animations. This paper considers several important metrics to evaluate the proposed approach’s effectiveness, including user satisfaction, computational efficiency, motion smoothness and realism. Comparative studies with classical animation methods showed that the method generates realistic movements on 2D characters while saving time during production. The numerical findings exemplify that the recommended 2D-AI-BM model improves an accuracy rate of 97.4%, computational efficiency ratio of 96.3%, motion control ratio of 95.4%, pose detection ratio of 94.8% and scalability ratio of 93.2% compared to other popular techniques.

In character animation education, the training process needs diverse domain knowledge to be covered in order to develop students with good animation ability. However, design of motion also requires digital ability, creativity and motion knowledge. Moreover, there are gaps between animation education and industry production which motion capture is widely applied. Here we try to incorporate motion capture into education and investigate whether motion capture is supportive in character animation, especially in creativity element. By comparing two kinds of motion design method, traditional keyframe and motion capture, we investigated students’ creativity in motion design. The results showed that in originality factor, keyframe method had slightly higher performance support for designing unusual motions. Nevertheless, motion capture had shown more support in creating valid actions in quantity which implied fluency factor of creativity was achieved. However, in flexibility factor, although motion capture created more emotions in amount, keyframe method actually offered higher proportion of sentiment design. Participants indicated that keyframe was helpful to design extreme poses. While motion capture provided intuitive design tool for exploring possibilities. Therefore, we propose to combine motion capture technology with keyframe method in character animation education to increase digital ability, stimulate creativity, and establish solid motion knowledge.

Human and animal presence in natural landscapes is initially revealed by the immediate impact of their locomotion, from footprints to crushed grass. In this work, we present an approach to model the effects of virtual characters on natural terrains, focusing on the impact of human locomotion. We introduce a lightweight solution to compute accurate foot placement on uneven ground and infer dynamic foot pressure from kinematic animation data and the mass of the character. A ground and vegetation model enables us to effectively simulate the local impact of locomotion on soft soils and plants over time, resulting in the formation of visible paths. As our results show, we can parameterize various soil materials and vegetation types validated with real-world data. Our method can be used to significantly increase the realism of populated natural landscapes and the sense of presence in virtual applications and games.

We introduce an animation system that transforms instructional videos into 3D animations with pedagogical agents, supplemented by animation editing tools to foster expressive, agent-based presentations, thereby enhancing educational benefits. Our system extracts the lecturer’s motion from the imported instructional video. Once the data extraction is complete, the system retargets this motion to the virtual agent and integrates it into a virtual classroom environment. Subsequently, it provides a GUI-based animation editing tool, offering a range of resources, such as motion assets (e.g., upper body gestures, facial expressions), which enable users to layer them on top of the extracted motion to make the pedagogical agent’s motions more engaging. To evaluate our system, we conducted a user study encompassing non-expert and expert groups, employing a mixed-method approach by collecting both quantitative and qualitative data. The results demonstrated our system’s educational value, functionality, and usability. Furthermore, the comparative analysis between the non-expert (people with no animation experience) and expert (people with prior animation experience) user groups provided distinct perspectives on our system, reflecting differences in the user’s animation experience and expertise. However, both groups reported similar usability and task load levels, indicating that non-experts can use our system efficiently to produce expressive agent-based presentations. We plan to release our system as an open source, cross-platform solution to help educators create engaging agent-based presentations.

This paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOFs) from a very low number of DOFs. To solve such a complex problem, the presented method is divided into several steps. The user’s full-body locomotion and the IMU’s data are recorded simultaneously. Then, the data is preprocessed in such a way that would be handled more efficiently. By developing a hierarchical multivariate hidden Markov model with reactive interpolation functionality the system learns the structure of the motion sequences. Specifically, the phases of the locomotion sequence are assigned in the higher hierarchical level, and the frame structure of the motion sequences are assigned at the lower hierarchical level. During the runtime of the method, the forward algorithm is used for reconstructing the full-body motion of a virtual character. Firstly, the method predicts the phase where the input motion belongs (higher hierarchical level). Secondly, the method predicts the closest trajectories and their progression and interpolates the most probable of them to reconstruct the virtual character’s full-body motion (lower hierarchical level). Evaluating the proposed method shows that it works on reasonable framerates and minimizes the reconstruction errors compared with previous approaches.

The increasing demand for lifelike character animation in digital media has driven the exploration of artificial intelligence techniques, particularly Generative Adversarial Networks (GANs), to enhance realism. However, existing GAN models often struggle to capture the dynamic and complex motion patterns required for high-quality animation. This study aims to compare the performance of three GAN variants, DCGAN, Pix2Pix, and StyleGAN, in generating realistic character animations using the Human 3.6 M dataset. The models were trained under uniform hyperparameters and evaluated using Fréchet Inception Distance (FID), Inception Score (IS), and Mean Squared Error (MSE). Experimental results show that StyleGAN outperformed the others, achieving the lowest FID (29.80 ± 5.08), the highest IS (3.99 ± 0.22), and the lowest MSE (0.019 ± 0.005), indicating superior visual realism, diversity, and motion accuracy. These findings demonstrate that StyleGAN offers a more effective solution for realistic character animation, with practical implications for its integration into film, video game, and virtual environment production workflows.

One of the pressing desires of content creators is to be able to visualize how their characters will look in a scene as soon as possible. In the early stages of film production, this desire can be partly achieved by the computer graphics-based process known as Pre-visualization ( Previz ). However, traditional previz necessitates a high level of expertise and is also time-consuming. This paper introduces the ASAP system, an automated tool that creates pre-visualized animations and storyboards by generating virtual character behavior/animations based on understanding the screenplay. The ASAP system parses the user-written screenplay to extract data, including character names, dialogue, actions, and emotions. This extracted data is then passed to the respective modules, which select virtual characters and automatically generate their speaking gestures, physical movements, and expressive behaviors. We demonstrate the system’s fidelity by presenting multiple outputs, including a 2D storyboard, a 3D preview, and a VR-based immersive scenario, along with simulations of potential use cases. The ASAP system can streamline pre-visualization tasks in the pre-production phase and has the potential to be widely adopted by the film industry.

Most of the human daily activities include acyclic multi-contact motions. Yet, generating such motions is challenging because of its high-dimensional and nonlinear solution space made by combinations of individual movements of body parts. In this paper, we present a novel keyframe-based framework to automatically generate multi-contact character motions. Our system consists of two components: key-pose planning and interpolation. Given initial and goal poses in which each contact can be repositioned at most one time during the transition, our key-pose planning step generates intermediate key-poses that represent contact changes, taking into account a set of principles for goal-directed movements. Next, the key-poses of each joint are independently interpolated to generate an acyclic multi-contact motion. We demonstrate that our framework can synthesize plausible interaction motions with a number of man-made objects, such as chairs and bicycles, without using any motion data. In addition, we show the scalability of our method by creating a long-term motion of climbing a ladder.

Restricted by the diversity and complexity of human behaviors, simulating a character to achieve human-level perception and motion control is still an active as well as a challenging area. We present a style-based teleoperation framework with the help of human perceptions and analyses to understand the tasks being handled and the unknown environment to control the character. In this framework, the motion optimization and body controller with center-of-mass and root virtual control (CR-VC) method are designed to achieve motion synchronization and style mimicking while maintaining the balance of the character. The motion optimization synthesizes the human high-level style features with the balance strategy to create a feasible, stylized, and stable pose for the character. The CR-VC method including the model-based torque compensation synchronizes the motion rhythm of the human and character. Without any inverse dynamics knowledge or offline preprocessing, our framework is generalized to various scenarios and robust to human behavior changes in real-time. We demonstrate the effectiveness of this framework through the teleoperation experiments with different tasks, motion styles, and operators. This study is a step toward building a human-robot interaction that uses humans to help characters understand and achieve the tasks.