FaceVR: Real-Time Facial Reenactment and Eye Gaze Control in Virtual Reality

We introduce FaceVR, a novel method for gaze-aware facial reenactment in the Virtual Reality (VR) context. The key component of FaceVR is a robust algorithm to perform real-time facial motion capture of an actor who is wearing a head-mounted display (HMD), as well as a new data-driven approach for eye tracking from monocular videos. In addition to these face reconstruction components, FaceVR incorporates photo-realistic re-rendering in real time, thus allowing artificial modifications of face and eye appearances. For instance, we can alter facial expressions, change gaze directions, or remove the VR goggles in realistic re-renderings. In a live setup with a source and a target actor, we apply these newly-introduced algorithmic components. We assume that the source actor is wearing a VR device, and we capture his facial expressions and eye movement in real-time. For the target video, we mimic a similar tracking process; however, we use the source input to drive the animations of the target video, thus enabling gaze-aware facial reenactment. To render the modified target video on a stereo display, we augment our capture and reconstruction process with stereo data. In the end, FaceVR produces compelling results for a variety of applications, such as gaze-aware facial reenactment, reenactment in virtual reality, removal of VR goggles, and re-targeting of somebody's gaze direction in a video conferencing call

EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment

Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time

Single-shot layered reflectance separation using a polarized light field camera

We present a novel computational photography technique for single shot separation of diffuse/specular reflectance as well as novel angular domain separation of layered reflectance. Our solution consists of a two-way polarized light field (TPLF) camera which simultaneously captures two orthogonal states of polarization. A single photograph of a subject acquired with the TPLF camera under polarized illumination then enables standard separation of diffuse (depolarizing) and polarization preserving specular reflectance using light field sampling. We further demonstrate that the acquired data also enables novel angular separation of layered reflectance including separation of specular reflectance and single scattering in the polarization preserving component, and separation of shallow scattering from deep scattering in the depolarizing component. We apply our approach for efficient acquisition of facial reflectance including diffuse and specular normal maps, and novel separation of photometric normals into layered reflectance normals for layered facial renderings. We demonstrate our proposed single shot layered reflectance separation to be comparable to an existing multi-shot technique that relies on structured lighting while achieving separation results under a variety of illumination conditions

Design and Development of a Multi-Sided Tabletop Augmented Reality 3D Display Coupled with Remote 3D Imaging Module

This paper proposes a tabletop augmented reality (AR) 3D display paired with a remote 3D image capture setup that can provide three-dimensional AR visualization of remote objects or persons in real-time. The front-side view is presented in stereo-3D format, while the left-side and right-side views are visualized in 2D format. Transparent glass surfaces are used to demonstrate the volumetric 3D augmentation of the captured object. The developed AR display prototype mainly consists of four 40 × 30 cm2 LCD panels, 54% partially reflective glass, an in-house developed housing assembly, and a processing unit. The capture setup consists of four 720p cameras to capture the front-side stereo view and both the left- and right-side views. The real-time remote operation is demonstrated by connecting the display and imaging units through the Internet. Various system characteristics, such as range of viewing angle, stereo crosstalk, polarization perseverance, frame rate, and amount of reflected and transmitted light through partially reflective glass, were examined. The demonstrated system provided 35% optical transparency and less than 4% stereo crosstalk within a viewing angle of ±20 degrees. An average frame rate of 7.5 frames per second was achieved when the resolution per view was 240 × 240 pixels

Design and Development of a Multi-Sided Tabletop Augmented Reality 3D Display Coupled with Remote 3D Imaging Module

This paper proposes a tabletop augmented reality (AR) 3D display paired with a remote 3D image capture setup that can provide three-dimensional AR visualization of remote objects or persons in real-time. The front-side view is presented in stereo-3D format, while the left-side and right-side views are visualized in 2D format. Transparent glass surfaces are used to demonstrate the volumetric 3D augmentation of the captured object. The developed AR display prototype mainly consists of four 40 × 30 cm2 LCD panels, 54% partially reflective glass, an in-house developed housing assembly, and a processing unit. The capture setup consists of four 720p cameras to capture the front-side stereo view and both the left- and right-side views. The real-time remote operation is demonstrated by connecting the display and imaging units through the Internet. Various system characteristics, such as range of viewing angle, stereo crosstalk, polarization perseverance, frame rate, and amount of reflected and transmitted light through partially reflective glass, were examined. The demonstrated system provided 35% optical transparency and less than 4% stereo crosstalk within a viewing angle of ±20 degrees. An average frame rate of 7.5 frames per second was achieved when the resolution per view was 240 × 240 pixels