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This article describes the possibilities and problems that occur using the SteamVR tracking 2.0 system as a camera tracking system in a virtual studio and explains an approach for implementation and calibration within a professional studio environment. The tracking system allows for cost effective deployment. Relevant application fields are also mixed reality recording and streaming of AR and VR experiences.
Acquiring human motion data from video images plays an important role in the field of computer vision. Ground truth tracking systems require markers to create high quality motion data. But in many applications it is desired to work without markers. In recent years affordable hardware for markerless tracking systems was made available at a consumer level. Efficient depth camera systems based on Time-of-Flight sensors and structured light systems have made it possible to record motion data in real time. However, the gap between the quality of marker-based and markerless systems is high. The error sources of a markerless motion tracking pipeline are discussed and a model-based filter is proposed, which adapts depending on spatial location. The proposed method is then proven to be more robust and accurate than the unfiltered data stream and can be used to visually enhance the presence of an actor within a virtual environment in live broadcast productions.
The article discusses the question of “How to convey the experience in a virtual environment to third parties?” and explains the different technical implementations which can be used for live streaming and recording of a mixed reality experience. The real-world applications of our approach include education, entertainment, e- sports, tutorials, and cinematic trailers, which can benefit from our research by finding a suitable solution for their needs. We explain and outline our Mixed Reality systems as well as discuss the experience of recorded demonstrations of different VR applications, including the need for calibrated camera lens parameters based on realtime encoder values.
Live-Video-Broadcasting mit mehreren Kameras erfordert eine Vielzahl von Fachkenntnissen. Robotersysteme ermöglichen zwar die Automatisierung von gängigen und wiederholten Tracking-Aufnahmen, diese erlauben jedoch keine kurzfristigen Anpassungen aufgrund von unvorhersehbaren Ereignissen. In diesem Beitrag wird ein modulares, automatisiertes Kamerasteuerungs- und Bildschnitt-System eingeführt, das auf grundlegenden kinematografischen Regeln basiert. Die Positionen der Akteure werden durch ein markerloses Tracking-System bereitgestellt. Darüber hinaus werden Tonpegel der Lavaliermikrofone der Akteure zur Analyse der aktuellen Szene verwendet. Ein Expertensystem ermittelt geeignete Kamerawinkel und entscheidet, wann von einer Kamera auf eine andere umgeschaltet werden soll. Eine Testproduktion wurde durchgeführt, um den entwickelten Prototyp in einem Live-Broadcast-Szenario zu beobachten und diente als Videodemonstration für eine Evaluierung.
Live video broadcasting requires a multitude of professional expertise to enable multi-camera productions. Robotic systems allow the automation of common and repeated tracking shots. However, predefined camera shots do not allow quick adjustments when required due to unpredictable events. We introduce a modular automated robotic camera control and video switch system, based on fundamental cinematographic rules. The actors' positions are provided by a markerless tracking system. In addition, sound levels of actors' lavalier microphones are used to analyse the current scene. An expert system determines appropriate camera angles and decides when to switch from one camera to another. A test production was conducted to observe the developed prototype in a live broadcast scenario and served as a video-demonstration for an evaluation.
In order to improve the interactivity between users and computers, recent technologies focus on incorporating gesture recognition into interactive systems. The aim of this article is to evaluate the effectiveness of using a Myo control armband and the Kinect 2 for recognition of gestures in order to interact with virtual objects in a weather report scenario. The Myo armband has an inertial measurement unit and is able to read electrical activity produced by skeletal muscles, which can be recognized as gestures, which are trained by machine learning. A Kinect sensor was used to build up a dataset which contains motion recordings of 8 different gestures and was also build up by a gesture training machine learning algorithm. Both input methods, the Kinect 2 and the Myo armband, were evaluated with the same interaction patterns in a user study, which allows a direct comparison and reveals benefits and limits of each technique.
Markerless talent tracking is widely used for interactions and animations within virtual environments. In a virtual (tv) studio talents could be overburden by interaction tasks because camera and text require extensive attention. We take a look into animations and inter- actions within a studio, which do not require any special attention or learning. We show the generation of an artificial shadow from a talent, which ease the keying process, where separation of real shadows from the background is a difficult task. We also demonstrate animations of footsteps and dust. Furthermore, capturing talents’ height can also be used to adjust the parameters of elements in the virtual environment, like the position and scaling of a virtual display. In addition to the talents, a rigid body was tracked as placeholder for graphics, easing the interaction tasks for a talent. Two test productions show the possibilities, which subtle animations offer. In the second production, the rendering was improved (shadows, filtering, normal maps, ...) and instead of using the rigid body to move an object (a flag), the animation was only controlled by the hand’s position.
A visual and spatial feedback system for orientation in virtual sets of virtual TV studios was developed and evaluated. It is based on a green proxy object, which moves around in the acting space by way of four transparent wires. A separate unit controls four winches and is connected to an engine, which renders the virtual set. A new developed plugin registers a virtual object’s position with the proxy object which imitates the virtual object’s movement on stage. This will allow actors to establish important eye contact with a virtual object and feel more comfortable in a virtual set. Furthermore, interaction with the virtual object and its proxy can be realised through a markerless actor tracking system. Several possible scenarios for user application were recorded and presented to experts in the broadcast industry, who evaluated the potential of SpiderFeedback in interviews and by questionnaires.