A Generative Human-Robot Motion Retargeting Approach Using a Single RGBD Sensor

The goal of human-robot motion retargeting is to let a robot follow the movements performed by a human subject. Typically in previous approaches, the human poses are precomputed from a human pose tracking system, after which the explicit joint mapping strategies are specified to apply the estimated poses to a target robot. However, there is not any generic mapping strategy that we can use to map the human joint to robots with different kinds of configurations. In this paper, we present a novel motion retargeting approach that combines the human pose estimation and the motion retargeting procedure in a unified generative framework without relying on any explicit mapping. First, a 3D parametric human-robot (HUMROB) model is proposed which has the specific joint and stability configurations as the target robot while its shape conforms the source human subject. The robot configurations, including its skeleton proportions, joint limitations, and DoFs are enforced in the HUMROB model and get preserved during the tracking procedure. Using a single RGBD camera to monitor human pose, we use the raw RGB and depth sequence as input. The HUMROB model is deformed to fit the input point cloud, from which the joint angle of the model is calculated and applied to the target robots for retargeting. In this way, instead of fitted individually for each joint, we will get the joint angle of the robot fitted globally so that the surface of the deformed model is as consistent as possible to the input point cloud. In the end, no explicit or pre-defined joint mapping strategies are needed. To demonstrate its effectiveness for human-robot motion retargeting, the approach is tested under both simulations and on real robots which have a quite different skeleton configurations and joint degree of freedoms (DoFs) as compared with the source human subjects

Realtime Face Tracking and Animation

Capturing and processing human geometry, appearance, and motion is at the core of computer graphics, computer vision, and human-computer interaction. The high complexity of human geometry and motion dynamics, and the high sensitivity of the human visual system to variations and subtleties in faces and bodies make the 3D acquisition and reconstruction of humans in motion a challenging task. Digital humans are often created through a combination of 3D scanning, appearance acquisition, and motion capture, leading to stunning results in recent feature films. However, these methods typically require complex acquisition systems and substantial manual post-processing. As a result, creating and animating high-quality digital avatars entails long turn-around times and substantial production costs. Recent technological advances in RGB-D devices, such as Microsoft Kinect, brought new hopes for realtime, portable, and affordable systems allowing to capture facial expressions as well as hand and body motions. RGB-D devices typically capture an image and a depth map. This permits to formulate the motion tracking problem as a 2D/3D non-rigid registration of a deformable model to the input data. We introduce a novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization. This led to unprecedented face tracking quality on a low cost consumer level device. The main drawback of this approach in the context of consumer applications is the need for an offline user-specific training. Robust and efficient tracking is achieved by building an accurate 3D expression model of the user's face who is scanned in a predefined set of facial expressions. We extended this approach removing the need of a user-specific training or calibration, or any other form of manual assistance, by modeling online a 3D user-specific dynamic face model. In complement of a realtime face tracking and modeling algorithm, we developed a novel system for animation retargeting that allows learning a high-quality mapping between motion capture data and arbitrary target characters. We addressed one of the main challenges of existing example-based retargeting methods, the need for a large number of accurate training examples to define the correspondence between source and target expression spaces. We showed that this number can be significantly reduced by leveraging the information contained in unlabeled data, i.e. facial expressions in the source or target space without corresponding poses. Finally, we present a novel realtime physics-based animation technique allowing to simulate a large range of deformable materials such as fat, flesh, hair, or muscles. This approach could be used to produce more lifelike animations by enhancing the animated avatars with secondary effects. We believe that the realtime face tracking and animation pipeline presented in this thesis has the potential to inspire numerous future research in the area of computer-generated animation. Already, several ideas presented in thesis have been successfully used in industry and this work gave birth to the startup company faceshift AG

Natural User Interfaces for Virtual Character Full Body and Facial Animation in Immersive Virtual Worlds

In recent years, networked virtual environments have steadily grown to become a frontier in social computing. Such virtual cyberspaces are usually accessed by multiple users through their 3D avatars. Recent scientific activity has resulted in the release of both hardware and software components that enable users at home to interact with their virtual persona through natural body and facial activity performance. Based on 3D computer graphics methods and vision-based motion tracking algorithms, these techniques aspire to reinforce the sense of autonomy and telepresence within the virtual world. In this paper we present two distinct frameworks for avatar animation through user natural motion input. We specifically target the full body avatar control case using a Kinect sensor via a simple, networked skeletal joint retargeting pipeline, as well as an intuitive user facial animation 3D reconstruction pipeline for rendering highly realistic user facial puppets. Furthermore, we present a common networked architecture to enable multiple remote clients to capture and render any number of 3D animated characters within a shared virtual environment

Lessons from digital puppetry - Updating a design framework for a perceptual user interface

While digital puppeteering is largely used just to augment full body motion capture in digital production, its technology and traditional concepts could inform a more naturalized multi-modal human computer interaction than is currently used with the new perceptual systems such as Kinect. Emerging immersive social media networks with their fully live virtual or augmented environments and largely inexperienced users would benefit the most from this strategy. This paper intends to define digital puppeteering as it is currently understood, and summarize its broad shortcomings based on expert evaluation. Based on this evaluation it will suggest updates and experiments using current perceptual technology and concepts in cognitive processing for existing human computer interaction taxonomy. This updated framework may be more intuitive and suitable in developing extensions to an emerging perceptual user interface for the general public

Markerless Facial Motion Capture

With the ever-rising capabilities of motion capture systems; this project explored markerless facial motion capture programs using the Kinect Sensor for Xbox. Many systems today still use markers and end up retargeting after a motion capture recording. This project used a simpler process of setting up and being able to display the effects live. An off-the-shelf system was built using a computer, a Kinect Sensor, a plug-in from Brekel, and Autodesk software. The first goal was to create a process that was able to capture and project live facial motion for fewer than $500 USD. Anything over$500 USD was considered to be more of a professional studio set-up. With an inexpensive setup, amateur users can do motion capture outside of a studio. The second goal was to observe the outcome of the audiences\u27 responses and see if interaction felt more mechanical than human