Motion-capture-based avatar control framework in third-person view virtual environments

Abstract

This paper presents a motion-capture-based control framework for third-person view virtual reality applications. Using motion capture devices, a user can directly control the full body motion of an avatar in virtual environments. In addition, using a thirdperson view, in which the user watches himself as an avatar on the screen, the user can sense his own movements and interactions with other characters and objects visually. However, there are still a few fundamental problems. First, it is difficult to realize physical interactions from the environment to the avatar. Second, it is also difficult for the user to walk around virtual environments because the motion capture area is very small compared to the virtual environments. This paper proposes a novel framework to solve these problems. We propose a tracking control framework in which the avatar is controlled so as to track input motion from a motion capture device as well as system generated motion. When an impact is applied to the avatar, the system finds an appropriate reactive motion and controls the weights of two tracking controllers in order to realize realistic and also controllable reactions. In addition, when the user walks in position, the system generates a walking motion for the controller to track. The walking speed and turn angle are also controlled through the user's walking gestures. Using our framework, the system generates seamless transitions between user controlled motions and system generated motions. In this paper, we also introduce a prototype application including a simplified optical motion capture system.

Enhancing spatial perception and user experience in video games with volumetric shadows

“…Although a first-person view could have less problems, being closer to our natural way of seeing the world, we agree with Oshita (2006) that a thirdperson view is optimal for many full body action games, because it allows the players to fully see how their movements are mapped to the avatar's movements and how the avatar reacts to the environment.…”

CuDi3D: Curvilinear displacement based approach for online 3D action detection

“…High latency causes in fact system feedback to lag behind user actions and thus significantly degrades the interactivity of the user experience. Therefore, several approaches were proposed so as to reliably recognize actions with minimal latency [4,21,3,22,23,24]. Ellis et al [3] proposed new algorithms for reducing latency for both presegmented and online action classification tasks.…”

Automatic Learning of Gesture Recognition Model Using SOM and SVM

“…Matsunaga et al [9] used an SVM to learn the conditions for transitions, whereas Oshita [12] used manually described fuzzy-based rules. However, as explained above, the state machine must be created manually.…”

“…Gesture recognition has many potential applications, such as gaming interfaces (for example Nintendo Wii, Microsoft Kinect, Sony Move), control interfaces for robots [1], and electronic devices (for example TVs, lights, air conditioning) [12], severance systems [5], and so on. In general, gesture recognition determines what kind of action the user is performing from various input signals such as body position, velocity and orientation.…”