Closed-loop Central Pattern Generator Control of Human Gaits in OpenSim Simulator

International audienceIn this paper, a new neuro-musculoskeletal gait simulation platform is presented. This platform is developed to reproduce healthy or altered walking gaits. It is based on an original model of central pattern generator able to generate variable rhythmic signals for controlling biological human leg joints. Output signals of motoneurons are applied to excitation inputs of modelled muscles of the human lower limbs model. Eight central pattern generators control a musculoskeletal model made up of three joints per leg actuated by 44 Hill-type muscle models. Forward dynamics simulation in OpenSim show that it is possible to generate different stable walking gaits by changing parameters of controller. Further work is aimed on development of stable human standing by implementing reflexes

An automatic tool to facilitate authoring animation blending in game engines

Achieving realistic virtual humans is crucial in virtual reality applications and video games. Nowadays there are software and game development tools, that are of great help to generate and simulate characters. They offer easy to use GUIs to create characters by dragging and drooping features, and making small modifications. Similarly, there are tools to create animation graphs and setting blending parameters among others. Unfortunately, even though these tools are relatively user friendly, achieving natural animation transitions is not straight forward and thus non-expert users tend to spend a large amount of time to generate animations that are not completely free of artefacts. In this paper we present a method to automatically generate animation blend spaces in Unreal engine, which offers two advantages: the first one is that it provides a tool to evaluate the quality of an animation set, and the second one is that the resulting graph does not depend on user skills and it is thus not prone to user errors.Peer ReviewedPostprint (author's final draft

Making Humanoid Robots More Acceptable Based on the Study of Robot Characters in Animation

In this paper we take an approach in Humanoid Robots are not considered as robots who resembles human beings in a realistic way of appearance and act but as robots who act and react like human that make them more believable by people. Regarding this approach we will study robot characters in animation movies and discuss what makes some of them to be accepted just like a moving body and what makes some other robot characters to be believable as a living human. The goal of this paper is to create a rule set that describes friendly, socially acceptable, kind, cute... robots and in this study we will review example robots in popular animated movies. The extracted rules and features can be used for making real robots more acceptable

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table

AGORASET: a dataset for crowd video analysis

International audienceThe ability of efficient computer vision tools (detec- tion of pedestrians, tracking, ...) as well as advanced rendering techniques have enabled both the analysis of crowd phenomena and the simulation of realistic sce- narios. A recurrent problem lies in the evaluation of those methods since few common benchmark are avail- able to compare and evaluate the techniques is avail- able. This paper proposes a dataset of crowd sequences with associated ground truths (individual trajectories of each pedestrians inside the crowd, related continuous quantities of the scene such as the density and the veloc- ity field). We chosed to rely on realistic image synthesis to achieve our goal. As contributions of this paper, a typology of sequences relevant to the computer vision analysis problem is proposed, along with images of se- quences available in the database

Motion enriching using humanoide captured motions

Animated humanoid characters are a delight to watch. Nowadays they are extensively used in simulators. In military applications animated characters are used for training soldiers, in medical they are used for studying to detect the problems in the joints of a patient, moreover they can be used for instructing people for an event(such as weather forecasts or giving a lecture in virtual environment). In addition to these environments computer games and 3D animation movies are taking the benefit of animated characters to be more realistic. For all of these mediums motion capture data has a great impact because of its speed and robustness and the ability to capture various motions. Motion capture method can be reused to blend various motion styles. Furthermore we can generate more motions from a single motion data by processing each joint data individually if a motion is cyclic. If the motion is cyclic it is highly probable that each joint is defined by combinations of different signals. On the other hand, irrespective of method selected, creating animation by hand is a time consuming and costly process for people who are working in the art side. For these reasons we can use the databases which are open to everyone such as Computer Graphics Laboratory of Carnegie Mellon University.Creating a new motion from scratch by hand by using some spatial tools (such as 3DS Max, Maya, Natural Motion Endorphin or Blender) or by reusing motion captured data has some difficulties. Irrespective of the motion type selected to be animated (cartoonish, caricaturist or very realistic) human beings are natural experts on any kind of motion. Since we are experienced with other peoples’ motions, and comparing each motion to the others, we can easily judge one individual’s mood from his/her body language. As being a natural master of human motions it is very difficult to convince people by a humanoid character’s animation since the recreated motions can include some unnatural artifacts (such as foot-skating, flickering of a joint)

Spinal muscular atrophy

Spinal Muscular Atrophy (SMA) is one of many neuromuscular diseases affect ing motor neurons and skeletal muscles. This disorder causes deterioration of the motor neurons (specifically the Anterior Horn Cells of the spinal cord). These motor neurons that control muscles are selectively destroyed resulting in varying degrees of atrophy, weakness and paralysis of the trunk and limb muscles. In this first half of the thesis report, various aspects of Spinal Muscular Atrophy, including history, anatomy/ physiology, diagnosis, theories of its function, types, characteristics, heredity, research, and treatment will be examined. A second portion of this report focuses upon both illustration and interactive computer media and animation as a visual introduction to SMA for the lay audience, patients, and their families