Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

Can We Distinguish Biological Motions of Virtual Humans? Biomechanical and Perceptual Studies With Captured Motions of Weight Lifting

International audiencePerception of biological motions is a key issue in order to evaluate the quality and the credibility of motions of virtual humans. This paper presents a perceptual study to evaluate if human beings are able to accurately distinguish differences in natural lifting motions with various masses in virtual environments (VE), which is not the case. However, they reached very close levels of accuracy when watching to computer animations compared to videos. Still, quotes of participants suggest that the discrimination process is easier in videos of real motions which included muscles contractions, more degrees of freedom, etc. These results can be used to help animators to design efficient physically-based animations

VCoach: A Customizable Visualization and Analysis System for Video-based Running Coaching

Videos are accessible media for analyzing sports postures and providing feedback to athletes. Existing video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers either manually by a coach or automatically by computing key parameters from poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. Besides, existing sport-specific systems with embedded bespoke pose attributes can hardly generalize to new attributes; directly overlaying two poses might not clearly visualize the key differences that viewers would like to pursue. To address these issues, we analyze and visualize human pose data with customizable viewpoints and attributes in the context of common biomechanics of running poses, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, VCoach, to provide feedback on running poses for amateurs. VCoach provides automatic low-level comparisons of the running poses between a novice and an expert, and visualizes the pose differences as part-based 3D animations on a human model. Meanwhile, it retains the users' controllability and customizability in high-level functionalities, such as navigating the viewpoint for previewing feedback and defining their own pose attributes through our interface. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system

TFF (v.4.1): A Mathematica Notebook for the Calculation of One- and Two-Neutron Stripping and Pick-Up Nuclear Reactions

The program TFF calculates stripping single-particle form factors for one-neutron transfer in prior representation with appropriate perturbative treatment of recoil. Coupled equations are then integrated along a semiclassical trajectory to obtain one- and two-neutron transfer amplitudes and probabilities within first- and second-order perturbation theory. Total and differential cross-sections are then calculated by folding with a transmission function (obtained from a phenomenological imaginary absorption potential). The program description, user instructions and examples are discussed

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)

Investigating the Informational Nature of a Modeled Visual Demonstration.

This study investigated the informational nature of a modeled visual demonstration of slalom-ski type movements performed on a ski simulator. Hypotheses exist suggesting that a model may convey information primarily about movement coordination (Newell, 1985), or movement form (Whiting, 1988), but there is no empirical evidence that this information is used by the learner so that skill acquisition is facilitated. To investigate this information question, three experiments were conducted that replicated and extended a study by Whiting, Bijlard, and den Brinker (1987) by analyzing movement kinematics of subjects in addition to movement outcome. In the first experiment, the expert model\u27s performance was analyzed. The second and third experiment investigated the acquisition of slalom-ski type movements for groups that observed the expert model on all 5 days, groups that observed the model only on day 1, and groups that learned the skill under discovery learning conditions. Results of movement outcome variables platform amplitude and frequency revealed that observing a model was advantageous over discovery learning. Analysis of movement kinematics suggested that the expert model may have conveyed information about the relative motion of torso and limbs, or movement coordination, that facilitated the acquisition of the slalom-ski type movements. Results further suggested that the coordination information the model may have conveyed was used early in learning, and that observing a model during later stages of learning was of no further benefit

Synthesizing Human Motion From Intuitive Constraints

Many compelling applications would become feasible if novice users had the ability to synthesize high quality human motion based only on a simple sketch and a few easily specified constraints. Motion graphs and their variations have proven to be a powerful tool for synthesizing human motion when only a rough sketch is given. Motion graphs are simple to implement, and the synthesis can be fully automatic. When unrolled into the environment, motion graphs, however, grow drastically in size. The major challenge is then searching these large graphs for motions that satisfy user constraints. A number of sub-optimal algorithms that do not provide guarantees on the optimality of the solution have been proposed. In this paper, we argue that in many situations to get natural results an optimal or nearly-optimal search is required. We show how to use the well-known A* search to find solutions that are optimal or of bounded sub-optimality. We achieve this goal for large motion graphs by performing a lossless compression of the motion graph and implementing a heuristic function that significantly accelerates the search for the domain of human motion. We demonstrate the power of this approach by synthesizing optimal or near optimal motions that include a variety of behaviors in a single motion. These experiments show that motions become more natural as the optimality improves