Patent Publication Number: US-2022239893-A1

Title: Reverse pass-through glasses for augmented reality and virtual reality devices

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present disclosure is related and claims priority under 35 U.S.C. § 119(e) to U.S. Prov. Appl. No. 63/142,458, to Nathan Matsuda, et al., filed on Jan. 27, 2021, the contents of which are hereby incorporated by reference in their entirety, for all purposes. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure is related to augmented reality (AR) and virtual reality (VR) devices including a reverse pass-through feature that provides a realistic view of a user&#39;s facial features to a forward onlooker. More specifically, the present disclosure provides an autostereoscopic external display for onlookers of an AR/VR headset user. 
     Related Art 
     In the field of AR and VR devices, some devices include outward facing displays that provide a view for an onlooker of the images being displayed for the user of the device. While these configurations facilitate a better understanding for an onlooker of what a user of the AR or VR device is experiencing, it leaves the onlooker clueless as to what is the state of mind of the user or focus of attention of the user, such as if the user is attempting to speak to the onlooker using a pass-through mode and is not otherwise engaged in a virtual reality environment. Moreover, for such devices having outward facing displays, they are typically traditional, two-dimensional displays lacking the realistic view of a full bodied image of at least a portion of the user&#39;s face or head, such as to portray the accurate depth and distance of the user&#39;s face or head within the device. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1A  illustrates an AR or VR device including an autostereoscopic external display, according to some embodiments. 
         FIG. 1B  illustrates a user of an AR or VR device as viewed by a forward onlooker, according to some embodiments. 
         FIG. 2  illustrates a detailed view of an eyepiece for an AR or VR device configured to provide a reverse pass-through view of the user&#39;s face to a forward onlooker, according to some embodiments. 
         FIGS. 3A-3D  illustrate different aspects and components of a micro lens array used to provide a reverse pass-through view of an AR or a VR device user to a forward onlooker, according to some embodiments. 
         FIG. 4  illustrates a ray-tracing view through a light field display to provide a wide-angle, high resolution view of an AR or a VR device user to a forward onlooker, according to some embodiments. 
         FIGS. 5A-5D  illustrate different aspects of a resolution power characteristic in a micro lens array used to provide a wide-angle, high resolution view of an AR or a VR device user, according to some embodiments. 
         FIG. 6  illustrates a 3D rendition of a portion of a face of an AR or VR device user, according to some embodiments. 
         FIG. 7  illustrates a block diagram of a model architecture used for a 3D rendition of a portion of a face of a VR/AR headset user, according to some embodiments. 
         FIGS. 8A-8D  illustrate elements and steps in a method for training a model to provide a view of a portion of a user&#39;s face to an auto stereoscopic display in a virtual reality headset, according to some embodiments. 
         FIG. 9  illustrates a flowchart in a method for providing an autostereoscopic view of a face of a VR/AR headset user, according to some embodiments. 
         FIG. 10  illustrates a flowchart in a method for rendering a three-dimensional (3D) view of a portion of a user&#39;s face from multiple, two-dimensional (2D) images of a portion of the user&#39;s face. 
         FIG. 11  illustrates a flowchart in a method for training a model to render a three-dimensional (3D) view of a portion of a user&#39;s face from multiple, two-dimensional (2D) images of a portion of the user&#39;s face, according to some embodiments. 
         FIG. 12  illustrates a computer system configured to perform at least some of the methods for using an AR or VR device, according to some embodiments. 
     
    
    
     In the figures, like elements are labeled likewise, according to their description, unless explicitly stated otherwise. 
     SUMMARY 
     In a first embodiment, a device includes a near-eye display configured to provide an image to a user and an eye imaging system configured to collect an image of a face of the user. The device also includes a light field display configured to provide an autostereoscopic image of a three-dimensional reconstruction of the face of the user to an onlooker. The autostereoscopic image depicts a perspective-corrected view of the user&#39;s face from multiple viewpoints within a field of view of the light field display. 
     In a second embodiment, a computer-implemented method includes receiving multiple two-dimensional images having one or more fields of view from a portion of a user&#39;s face and extracting multiple image features from the two-dimensional images at a first resolution setting. The computer-implemented method also includes rendering, for a portion of the user&#39;s face, a three-dimensional reconstruction based on the image features, wherein the portion of the user&#39;s face is obscured to an onlooker, and providing, to the onlooker, an autostereoscopic image of the three-dimensional reconstruction of the user&#39;s face. 
     In a third embodiment, a computer-implemented method is used for training a model to provide a view of a portion of a user&#39;s face to an auto stereoscopic display in a virtual reality headset. The computer-implemented method includes collecting, from a face of multiple users, multiple ground-truth images and rectifying the ground-truth images with stored, calibrated stereoscopic pairs of images. The computer-implemented method also includes generating, with a three-dimensional face model, multiple synthetic views of the face of the users, wherein the synthetic views of the face of the users match a geometry of a face imaging camera in the virtual reality headset, and the three-dimensional face model is texture mapped with the ground-truth images. The computer-implemented method also includes training the three-dimensional face model based on a difference between the ground-truth images and the synthetic views of the face of the users. 
     In yet another embodiment, a system includes a first means for storing instructions and a second means for executing the instructions to perform a method, the method includes collecting, from a face of multiple users, multiple ground-truth images and rectifying the ground-truth images with stored, calibrated stereoscopic pairs of images. The method also includes generating, with a three-dimensional face model, multiple synthetic views of the face of the users, wherein the synthetic views of the face of the users match a geometry of a face imaging camera in the virtual reality headset, and the three-dimensional face model is texture mapped with the ground-truth images. The method also includes training the three-dimensional face model based on a difference between the ground-truth images and the synthetic views of the face of the users. 
     DETAILED DESCRIPTION OF THE FIGURES 
     In the following detailed description, numerous specific details are set forth to provide a full understanding of the present disclosure. It will be apparent, however, to one ordinarily skilled in the art, that embodiments of the present disclosure may be practiced without some of these specific details. In other instances, well-known structures and techniques have not been shown in detail so as not to obscure the disclosure. 
     In the field of AR and VR devices and uses thereof, there exists a disconnection between the user and the environment that may be annoying to people surrounding the user, if not hazardous for the user and others nearby. In some scenarios, it may be desirable for the user to engage one or more onlookers for conversation, or attention. Current AR and VR devices lack the ability for onlookers to engage and to verify the focus of attention of the user. 
     Typically, display applications trying to match a wide-angle field of view or three-dimensional displays with a deep focal distance need to compromise on spatial resolution of the display. One approach is to reduce the size of the pixels in the display to increase the resolution; however, the pixel size in current state-of-the-art technology is reaching the diffraction limit of visible and near infrared light, which imposes a limit to the ultimate resolution that can be achieved. In the case of AR and VR devices, this compromise between spatial resolution and angular resolution is less constringent, given the limited ranges associated with the form factor and angular dimensions involved in these devices. 
     A desirable feature of an AR/VR device is to have a small form factor. Accordingly, thinner devices are desirable. To achieve this, multi-lenslet array (MLA) light field displays, having a shorter working distance, provide a thin cross section of a VR headset with limited resolution loss by using convenient designs of holographic pancake lenses. 
     Another desirable feature of an AR/VR device is to provide high resolution. Although this imposes a limit on the depth of focus, this limitation, common in optical systems used to capture complex scenery, is less stringent for an external display disclosed herein because the depth of field is limited by the relative location between the external display and the user&#39;s face, which varies little. 
     Embodiments as disclosed herein improve the quality of in-person interaction using VR headsets for a wide variety of applications wherein one or more people wearing a VR headset interact with one or more people not wearing a VR headset. Embodiments as discussed herein remove the friction between VR users and onlookers or other VR users, and bridge the gap between VR and AR: co-presence benefits of see-through AR with more finesse and higher immersion capacity of VR systems. Accordingly, embodiments as disclosed herein provide a compelling and more natural VR experience. 
     More generally, embodiments as disclosed herein provide an AR/VR headset that looks like a standard pair of see-through glasses to the onlooker, enabling a better engagement of the AR/VR user with the surrounding environment. This is highly helpful in scenarios where AR/VR users interact with other people or onlookers. 
       FIG. 1A  illustrates a headset  10 A including autostereoscopic external displays  110 A, according to some embodiments. Headset  10 A may be an AR or VR device configured to be mounted on a user&#39;s head. Headset  10 A includes two eyepieces  100 A mechanically coupled by a strap  15  and having a flexible mount to hold electronics components  20  in the back of the user&#39;s head. A flex connector  5  may electronically couple eyepieces  100 A with electronic components  20 . Each of eyepieces  100 A include eye imaging systems  115 - 1  and  115 - 2  (hereinafter, collectively referred to as “eye imaging systems  115 ”), configured to collect an image of a portion of a face of the user reflected from an optical surface in a selected field of view (FOV). Eye imaging systems  115  may include dual eye cameras that collect two images of the eye of the user at different FOVs, so as to generate a three-dimensional, stereoscopic view of at least a portion of the user&#39;s face. Eye imaging systems  115  may provide information about pupil location and movement to the electronics components. Eyepieces  100 A may also include external displays  110 A (e.g., a light field display) adjacent to the optical surface and configured to project an autostereoscopic image of the face of the user forward from the user. 
     In some embodiments, electronics components  20  may include a memory circuit  112  storing instructions and a processor circuit  122  that executes the instructions to receive the image of the portion of the face of the user from eye imaging systems  115 , and provide to external displays  110 A the autostereoscopic image of the face of the user. Moreover, electronics components  20  may also receive the image from the portion of the user&#39;s face from the one or more eye cameras, and apply image analysis to assess gaze, vergence, and focus by the user on an aspect of the exterior view, or a virtual reality display. In some embodiments, electronics components  20  include a communications module  118  configured to communicate with a network. Communications module  118  may include radio-frequency software and hardware to wirelessly communicate memory  112  and processor  122  with an external network, or some other device. Accordingly, communications module  118  may include radio antennas, transceivers, and sensors, and also digital processing circuits for signal processing according to any one of multiple wireless protocols such as Wi-Fi, Bluetooth, Near field contact (NFC), and the like. In addition, communications module  118  may also communicate with other input tools and accessories cooperating with headset  10 A (e.g., handle sticks, joysticks, mouse, wireless pointers, and the like). 
     In some embodiments, eyepieces  100 A may include one or more exterior cameras  125 - 1  and  125 - 2  (hereinafter, collectively referred to as “exterior cameras  125 ”) to capture a front view of a scene for the user. In some embodiments, exterior cameras  125  may focus or be directed to (e.g., by processor  122 ) aspects of the front view that the user may be particularly interested in, based on the gaze, vergence, and other features of the user&#39;s view that may be derived from the image of the portion of the user&#39;s face provided by the dual eye camera. 
       FIG. 1B  illustrates a headset  10 B as viewed by a forward onlooker, according to some embodiments. In some embodiments, headset  10 B may be an AR or VR device in a “snorkel” configuration. Hereinafter, headsets  10 A and  10 B will be collectively referred to as “headsets  10 .” In some embodiments, a visor  100 B may include a single forward display  110 B that provides a view of user  101  to an onlooker  102 . Display  110 B includes a portion of the face having the two eyes, a portion of the nose, eyebrows, and other facial features of user  101 . Further, an autostereoscopic image  111  of the user&#39;s face may include details such as an accurate and real-time position of the user&#39;s eyes, indicating a gaze direction and a vergence or focus of attention of user  101 . This may indicate to onlooker  102  whether the user is paying attention to something that has been said, or some other environmental disturbance or sensorial input that may draw the user&#39;s attention. 
     In some embodiments, autostereoscopic image  111  offers a 3D rendering of the face of the user. Accordingly, onlooker  102  has a full body view of the user&#39;s face and even the user&#39;s head, changing perspective as onlooker  102  changes an angle of view. In some embodiments, the outwardly projected display  110 B may include image features additional to the image of a portion of the user&#39;s face. For example, in some embodiments, the outwardly projected display may include virtual elements in the image superimposed to the image of the user&#39;s face (e.g., a reflection or glare of the virtual image that the user is actually viewing, or of a real light source in the environment). 
       FIG. 2  illustrates a detailed view of an eyepiece  200  for an AR or VR device configured to provide a reverse pass-through view of the user&#39;s face to a forward onlooker (cf. eyepieces  100 A and snorkel visor  100 B), according to some embodiments. Eyepiece  200  includes an optical surface  220  configured to provide an image to a user on a first side (to the left) of optical surface  220 . In some embodiments, the image to the user may be provided by a forward camera  225 , and optical surface  220  may include a display coupled to forward camera  225 . In some embodiments, the image in optical surface  220  may be a virtual image provided by a processor executing instructions stored in a memory (e.g., for VR devices, memory  112  and processor  122 ). In some embodiments (e.g., for AR devices), the image to the user may include, at least partially, an image transmitted from the front side of eyepiece  200  via transparent optical components (e.g., lenses, waveguides, prisms, and the like). 
     In some embodiments, eyepiece  200  also includes a first eye camera  215 A and a second eye camera  215 B (hereinafter, collectively referred to as “eye cameras  215 ”) configured to collect first and second images of the user&#39;s face (e.g., the eye of the user) at two different FOVs. In some embodiments, eye cameras  215  may be infrared cameras collecting images of the user&#39;s face in reflection mode, from a hot mirror assembly  205 . An illumination ring  211  may provide illumination to the portion of the user&#39;s face that is going to be imaged by eye cameras  215 . Accordingly, optical surface  220  may be configured to be reflective at the wavelength of light operated by eye cameras  215  (e.g., the infrared domain), and transmissive of light providing an image to the user, e.g., the visible domain, including Red (R), Blue (B), and Green (G) pixels. A forward display  210 B projects an autostereoscopic image of the face of the user to an onlooker (to the right end of the figure). 
       FIGS. 3A-3D  illustrate different aspects and components of a micro lens array  300  used as a screen to provide a reverse pass-through view of a user in an AR or a VR device to a forward onlooker, according to some embodiments. In some embodiments, micro lens array  300  receives light from a pixel array  320  and provides the image of the face of the user to the onlooker. In some embodiments, the image of the face of the user is a perspective view of a 3D rendition of the face of the user, depending on the onlooker angle of view. 
       FIG. 3A  is a detailed view of micro lens array  300  and includes multiple micro lenses  301 - 1 ,  301 - 2 , and  301 - 3  (collectively referred to, hereinafter, as “micro lenses  301 ”) arranged in a two-dimensional pattern  302  having a pitch  305 . In some embodiments, an aperture mask  315  may be disposed adjacent to the micro lens array such that one aperture is aligned with each micro lens  301 , to avoid cross-talk of different angles of view from the point of view of the onlooker. 
     For illustrative purposes only, pattern  302  is a hexagonal lattice of micro lenses  301  having a pitch  305  of less than a millimeter (e.g., 500 μm). Micro lens array  300  may include a first surface and a second surface  310  including concavities forming micro lenses  301 , the first and second surfaces  310  separated by a transmissive substrate  307  (e.g., N-BK7 glass, plastic, and the like). In some embodiments, transmissive substrate  307  may have a thickness of about 200 μm. 
       FIG. 3B  is a detailed view of a light field display  350  for use in a reverse pass-through headset, according to some embodiments. Light field display  350  includes a pixel array  320  adjacent to a micro lens array (e.g., micro lens array  300 ), of which only a micro lens  301  is shown, for illustrative purposes. Pixel array  320  includes multiple pixels  321  generating light beams  323  directed to micro lens  301 . In some embodiments, a distance  303  between pixel array  320  and micro lens  301  may be approximately equal to the focal length of micro lens  301 , and therefore outgoing light beams  325  may be collimated in different directions, depending on the specific position of the originating pixel  321 . Accordingly, different pixels  321  in pixel array  320  may provide a different angle of view of a 3D representation of the user&#39;s face, depending on the location of the onlooker. 
       FIG. 3C  is a plan view of micro lens array  300 , showing a honeycomb pattern. 
       FIG. 3D  illustrates micro lens array  300  with aperture mask  315  disposed adjacent to it so that openings on aperture mask  315  are centered on micro lens array  300 . In some embodiments, aperture mask  315  may include chrome, having apertures of about 400 μm over a 500 μm hex-pack pitch (as illustrated). Aperture mask  315  may be lined up with the first surface or the second surface  310 , on either side of micro lens array  300 , or on both sides. 
       FIG. 4  illustrates a ray-tracing view of a light field display  450  to provide a reverse pass-through image of the face of a user of an AR/VR device to an onlooker, according to some embodiments. Light field display  450  includes a micro lens array  400  used to provide a wide-angle, high resolution view of the face of a user of an AR or a VR device, to a forward onlooker, according to some embodiments. Micro-lens array  400  includes multiple micro-lenses  401  arranged in a two dimensional pattern, as disclosed herein. A pixel array  420  may include multiple pixels  421  providing light rays  423  that are transmitted through micro lens array  400  to generate a 3D rendition of at least a portion of a face of the user of the AR or VR device. Micro lens array  400  may include aperture mask  415 . Aperture mask  415  provides blocking elements near the edges of each of the micro lenses in micro lens array  400 . The blocking elements reduce the amount of light rays  425 B and  425 C relative to light rays  425 A forming the front view of the user&#39;s face, for the onlooker. This reduces cross talk and ghosting effects for an onlooker situated in front of the screen and looking at the 3D rendition of the user&#39;s face (down, according to  FIG. 4 ). 
       FIGS. 5A-5C  illustrate different aspects of a resolution power characteristic  500 A,  500 B, and  500 C (hereinafter, collectively referred to as “resolution power characteristics  500 ”) in a micro lens array to provide a wide-angle, high resolution view of the face of a user of an AR or a VR device, according to some embodiments. The abscissae  521  (X-axis), in resolution power characteristics  500  indicates an image distance (in mm) between the user&#39;s face (e.g., the eye of the user) and the micro lens array. The ordinates  522  (Y-axis), in resolution power characteristics  500  is the resolution of the optical system including the light display and the screen given in terms of a frequency value, e.g., feature cycles on the display, per millimeter (cycles/mm), as viewed by an onlooker situated about a meter away from the user wearing the AR or VR device. 
       FIG. 5A  illustrates resolution power characteristic  500 A including a cutoff value, which is the highest frequency that the onlooker may distinguish from the display. Curves  501 - 1 A and  501 - 2 A (hereinafter, collectively referred to as “curves  501 A”) are associated with two different headset models (referred to as Model 1 and Model 2, respectively). The specific resolution depends on the image distance and other parameters of the screen, such as the pitch of the micro lens array (e.g., pitch  305 ). In general, for a larger distance between the eye of the user and the screen, the resolution cutoff will monotonically decrease (to the right along abscissae  521 ). This is illustrated by the difference in cutoff values  510 - 2 A (approx. 0.1 cycles/mm) for curve  501 - 2 A, and  510 - 1 A (approx. 0.25 cycles/mm) for curve  501 - 1 A. Indeed, the headset model for curve  501 - 2 A has a larger image distance (close to 10 cm between user face and display) than the headset model for curve  501 - 1 A (about 5 cm between user eyes and display). Also, for a micro lens array having a wider pitch (Model 2, 500 μm pitch), the resolution cutoff will be reduced relative to a smaller pitch (Model 1, 200 μm pitch). 
       FIG. 5B  illustrates resolution power characteristic  500 B including a curve  501 B for a light field display model (Model 3) providing a spatial frequency of about 0.3 cycles/mm with an image distance of about 5 cm at point  510 B. 
       FIG. 5C  illustrates resolution power characteristic  500 C including curves  501 - 1 C,  501 - 2 C,  501 - 3 C, and  501 - 4 C (hereinafter, collectively referred to as “curves  501 C”). The abscissae  521 C (X-axis) for resolution power characteristics  500 C indicates a headset depth (e.g., similar to a distance between the user&#39;s eyes/face and the light field display), and the ordinates  522 C (Y-axis) indicate a pixel pitch (in microns, μm) for the pixel array in the light field display. Each one of curves  501 C indicates a number of cycles/mm cutoff resolution for each light field display model. Point  510 B is illustrated in comparison with a better resolution obtained at point  510 C for a light field display model (Model 4) with high density pixel packing (less than 10 μm pitch), and a close headset depth of about 25 mm (e.g., about one inch or less). 
       FIG. 5D  illustrates images  510 - 1 D and  510 - 2 D of the user wearing the headset according to an onlooker, for each of light field display models. Image  510 - 1 D is obtained with Model 3, and image  510 - 2 D is obtained with Model 4, of the light field display (cf. points  510 B and  510 C, respectively). The resolution performance of Model 4 is certainly better than that of Model 3, indicating that there is a wide range of possibilities to accommodate a desired resolution in view of other trade-offs in terms of model design, consistent with the present disclosure. 
       FIG. 6  illustrates a 3D rendition  621 A and  621 B (hereinafter, collectively referred to as “3D rendition  621 ”) of a portion of a face of a user of an AR or VR device, according to some embodiments. In some embodiments, 3D rendition  621  is provided by a model  650  operating on multiple 2D images  611  of at least a portion of the user&#39;s face (e.g., the eyes), and provided by an eye imaging system in the AR or VR device (cf. eye imaging system  115  and eye cameras  215 ). Model  650  may include linear and/or nonlinear algorithms such as neural networks (NN), convolutional neural networks (CNN), machine learning (ML) models, and artificial intelligence (AI) models. Model  650  includes instructions stored in a memory circuit and executed by a processor circuit. The memory circuit and the processor circuit may be stored in the back of the AR or VR device (e.g., memory  112  and processor  122  in electronics components  20 ). 
     Accordingly, multiple 2D images  611  are received from the eye imaging system to create, update, and improve model  650 . The multiple 2D images include at least two different FOVs, e.g., coming from each of two different stereoscopic eye cameras in the eye imaging system, and model  650  can determine which image came from which camera, to form 3D rendition  621 . Model  650  then uses the 2D image input and detailed knowledge of the difference between the FOVs of the two eye cameras (e.g., a camera direction vector) to provide 3D rendition  621  of at least a portion of the face of the user of the AR or VR device. 
       FIG. 7  illustrates a block diagram of a model architecture  700  used for a 3D rendition of a portion of a face of a user of a VR/AR headset, according to some embodiments. Model architecture  700  is split into two tracks  750 A and  750 B (hereinafter, collectively referred to as “tracks  750 ”), each for a different camera view of the user&#39;s face. The two tracks  750  are then combined by sharing weights in the model  755 . Model architecture  700  may include several stages, as follows: 
     Stage  702 : Extract image features (e.g., in 2D) at a lower resolution (e.g., with an infrared camera) using a Siamese network. In some embodiments, stage  702  includes down-sampling  712  the feature set over several neural network layers, for a set of input images  711  (e.g., training images or sampling images). 
     Stage  704 : Build a cost volume at the lower resolution by matching the features along scan lines in the images, to provide a coarse disparity estimate. 
     Stage  706 : Refine the results hierarchically to recover small details and thin structures. 
       FIGS. 8A-8D  illustrate elements and steps in a method for training a model to provide a view of a portion of a user&#39;s face to an autostereoscopic display in a virtual reality headset, according to some embodiments. An eyepiece  800  is trained with multiple training images  811  from multiple users. A 3D model  821  for each of the users is created including a texture map and a depth map to recover fine details of the image features  833 - 1 B,  833 - 2 B, and  833 C (hereinafter, collectively referred to as “texture and depth maps  833 ”). When 3D model  821  is generated, an autostereoscopic image of the three-dimensional reconstruction of the user&#39;s face is provided to a pixel array in a light field display. The light field display is separated into multiple segments of active pixels, each segment providing a portion of a field of view of 3D model  821  in a selected angle of view for the onlooker. 
       FIG. 8A  illustrates a setup  850  for collecting multiple training images  811  onto eyepiece  800 , according to some embodiments. Training images  811  may be provided by a display, and projected onto a screen  812  disposed at the same location as the hot mirror will be when the eyepiece is assembled in the headset. One or more infrared cameras  815  collect training images  811  in reflection mode, and one or more RGB cameras  825  collect training images in transmission mode. Setup  850  has an image vector  801 - 1 , an IR camera vector  801 - 2 , and an RGB camera vector  801 - 3  (hereinafter, collectively referred to as “positioning vectors  801 ”), fixed for all training images  811 . Positioning vectors  801  are used by the algorithm models to accurately assess sizes, distances, and angles of view associated with 3D model  821 . 
       FIG. 8B  illustrates texture and depth images  833 - 1 B and  833 - 2 B, according to some embodiments. Texture image  833 - 1 B may be obtained from a capture of a training image using RGB camera  825 , and depth image  833 - 2 B may be obtained from a training image using IR camera  815 . 
       FIG. 8C  illustrates a depth image  833 C collected with IR camera  815 , according to some embodiments. 
       FIG. 8D  illustrates 3D model  821  formed in relation to eyepiece  800 , according to some embodiments. 
       FIG. 9  illustrates a flowchart in a method  900  for providing an autostereoscopic view of a face of a VR/AR headset user, according to some embodiments. Steps in method  900  may be performed at least partially by a processor executing instructions stored in a memory, wherein the processor and the memory are part of electronics components in a headset as disclosed herein (e.g., memory  112 , processor  122 , electronics components  20 , and headsets  10 ). In yet other embodiments, at least one or more of the steps in a method consistent with method  900  may be performed by a processor executing instructions stored in a memory wherein at least one of the processor and the memory are remotely located in a cloud server, and the headset device is communicatively coupled to the cloud server via a communications module coupled to a network (cf. communications module  118 ). In some embodiments, method  900  may be performed using a model including a neural network architecture in a machine learning or artificial intelligence algorithm, as disclosed herein (e.g., model  650 , model architecture  700 ). In some embodiments, methods consistent with the present disclosure may include at least one or more steps from method  900  performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time. 
     Step  902  includes receiving multiple two-dimensional images having one or more fields of view from a portion of a user&#39;s face. 
     Step  904  includes extracting multiple image features from the two-dimensional images at a first resolution setting. In some embodiments, step  904  includes matching the image features along a scan line to build a cost volume at a first resolution setting and to provide a coarse disparity estimate. In some embodiments, step  904  includes recovering one or more image features including small details and thin structures at a second resolution setting that is higher than the first resolution setting. In some embodiments, step  904  includes generating a texture map of the portion of the user&#39;s face and a depth map of the portion of the user&#39;s face based on the image features, wherein the texture map includes a color detail of the image features and the depth map includes a depth location of the image features. 
     Step  906  includes rendering, for a portion of the user&#39;s face, a three-dimensional reconstruction based on the image features, wherein the portion of the user&#39;s face is obscured to an onlooker. 
     Step  908  includes providing, to the onlooker, an autostereoscopic image of the three-dimensional reconstruction of the user&#39;s face. In some embodiments, step  908  includes providing, to one segment of a light field display, a portion of a field of view of the user&#39;s face at a selected viewpoint for the onlooker. In some embodiments, step  908  further includes tracking one or more onlookers to identify an angle of view and modify a light field display to optimize a field of view for each of the one or more onlookers. 
       FIG. 10  illustrates a flowchart in a method  1000  for rendering a three-dimensional (3D) view of a portion of a user&#39;s face from multiple, two-dimensional (2D) images of a portion of the user&#39;s face. Steps in method  1000  may be performed at least partially by a processor executing instructions stored in a memory, wherein the processor and the memory are part of electronics components in a headset as disclosed herein (e.g., memory  112 , processor  122 , electronics components  20 , and headsets  10 ). In yet other embodiments, at least one or more of the steps in a method consistent with method  1000  may be performed by a processor executing instructions stored in a memory wherein at least one of the processor and the memory are remotely located in a cloud server, and the headset device is communicatively coupled to the cloud server via a communications module coupled to a network (cf. communications module  118 ). In some embodiments, method  1000  may be performed using a model including a neural network architecture in a machine learning or artificial intelligence algorithm, as disclosed herein (e.g., model  650 , model architecture  700 ). In some embodiments, methods consistent with the present disclosure may include at least one or more steps from method  1000  performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time. 
     Step  1002  includes receiving multiple 2D images having at least two or more fields of view. 
     Step  1004  includes extracting image features from the 2D images at a first resolution setting. 
     Step  1006  includes matching the image features along a scan line, to build a cost volume at the first resolution setting and to provide a coarse disparity estimate. 
     Step  1008  includes recovering one or more image features including small details and thin structures at a second resolution setting that is higher than the first resolution setting. 
       FIG. 11  illustrates a flowchart in a method  1100  for training a model to render a three-dimensional (3D) view of a portion of a user&#39;s face from multiple, two-dimensional (2D) images of a portion of the user&#39;s face, according to some embodiments. Steps in method  1100  may be performed at least partially by a processor executing instructions stored in a memory, wherein the processor and the memory are part of electronics components in a headset as disclosed herein (e.g., memory  112 , processor  122 , electronics components  20 , and headsets  10 ). In yet other embodiments, at least one or more of the steps in a method consistent with method  1100  may be performed by a processor executing instructions stored in a memory wherein at least one of the processor and the memory are remotely located in a cloud server, and the headset device is communicatively coupled to the cloud server via a communications module coupled to a network (cf. communications module  118 ). In some embodiments, method  1100  may be performed using a model including a neural network architecture in a machine learning or artificial intelligence algorithm, as disclosed herein (e.g., model  650 , model architecture  700 ). In some embodiments, methods consistent with the present disclosure may include at least one or more steps from method  1100  performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time. 
     Step  1102  includes collecting, from a face of multiple users, multiple ground-truth images. In some embodiments, step  1102  includes collecting a color image and an infrared image from a color display from a portion of a user&#39;s face. 
     Step  1104  includes rectifying the ground-truth images with stored, calibrated stereoscopic pairs of images. 
     Step  1106  includes generating, with a three-dimensional face model, multiple synthetic views of the face of the users, wherein the synthetic views of the face of the users match a geometry of a face imaging camera in the virtual reality headset, and the three-dimensional face model is texture mapped with the ground-truth images. In some embodiments, step  1106  includes generating a texture map and a depth map for each of the ground-truth images, wherein the texture map includes a color, a transparency, and a reflectance, and the depth map includes a virtual depth location, of each pixel in the ground-truth images. 
     Step  1108  includes training the three-dimensional face model based on a difference between the ground-truth images and the synthetic views of the face of the users. In some embodiments, step  1108  includes adjusting a coefficient in the three-dimensional face model based on a loss value between the ground-truth images and the synthetic views of the face of the users. 
     Hardware Overview 
       FIG. 12  is a block diagram illustrating an exemplary computer system  1200  with which headsets  10 , and methods  900 ,  1000 , and  1100  can be implemented. In certain aspects, computer system  1200  may be implemented using hardware or a combination of software and hardware, either in a dedicated server, or integrated into another entity, or distributed across multiple entities. Computer system  1200  may include a desktop computer, a laptop computer, a tablet, a phablet, a smartphone, a feature phone, a server computer, or otherwise. A server computer may be located remotely in a data center or be stored locally. 
     Computer system  1200  includes a bus  1208  or other communication mechanism for communicating information, and a processor  1202  (e.g., processor  122 ) coupled with bus  1208  for processing information. By way of example, the computer system  1200  may be implemented with one or more processors  1202 . Processor  1202  may be a general-purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information. 
     Computer system  1200  can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them stored in an included memory  1204  (e.g., memory  112 ), such as a Random Access Memory (RAM), a flash memory, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device, coupled with bus  1208  for storing information and instructions to be executed by processor  1202 . The processor  1202  and the memory  1204  can be supplemented by, or incorporated in, special purpose logic circuitry. 
     The instructions may be stored in the memory  1204  and implemented in one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, the computer system  1200 , and according to any method well known to those of skill in the art, including, but not limited to, computer languages such as data-oriented languages (e.g., SQL, dBase), system languages (e.g., C, Objective-C, C++, Assembly), architectural languages (e.g., Java, .NET), and application languages (e.g., PHP, Ruby, Perl, Python). Instructions may also be implemented in computer languages such as array languages, aspect-oriented languages, assembly languages, authoring languages, command line interface languages, compiled languages, concurrent languages, curly-bracket languages, dataflow languages, data-structured languages, declarative languages, esoteric languages, extension languages, fourth-generation languages, functional languages, interactive mode languages, interpreted languages, iterative languages, list-based languages, little languages, logic-based languages, machine languages, macro languages, metaprogramming languages, multiparadigm languages, numerical analysis, non-English-based languages, object-oriented class-based languages, object-oriented prototype-based languages, off-side rule languages, procedural languages, reflective languages, rule-based languages, scripting languages, stack-based languages, synchronous languages, syntax handling languages, visual languages, with languages, and xml-based languages. Memory  1204  may also be used for storing temporary variable or other intermediate information during execution of instructions to be executed by processor  1202 . 
     A computer program as discussed herein does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. 
     Computer system  1200  further includes a data storage device  1206  such as a magnetic disk or optical disk, coupled with bus  1208  for storing information and instructions. Computer system  1200  may be coupled via input/output module  1210  to various devices. Input/output module  1210  can be any input/output module. Exemplary input/output modules  1210  include data ports such as USB ports. The input/output module  1210  is configured to connect to a communications module  1212 . Exemplary communications modules  1212  include networking interface cards, such as Ethernet cards and modems. In certain aspects, input/output module  1210  is configured to connect to a plurality of devices, such as an input device  1214  and/or an output device  1216 . Exemplary input devices  1214  include a keyboard and a pointing device, e.g., a mouse or a trackball, by which a consumer can provide input to the computer system  1200 . Other kinds of input devices  1214  can be used to provide for interaction with a consumer as well, such as a tactile input device, visual input device, audio input device, or brain-computer interface device. For example, feedback provided to the consumer can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the consumer can be received in any form, including acoustic, speech, tactile, or brain wave input. Exemplary output devices  1216  include display devices, such as an LCD (liquid crystal display) monitor, for displaying information to the consumer. 
     According to one aspect of the present disclosure, headsets  10  can be implemented, at least partially, using a computer system  1200  in response to processor  1202  executing one or more sequences of one or more instructions contained in memory  1204 . Such instructions may be read into memory  1204  from another machine-readable medium, such as data storage device  1206 . Execution of the sequences of instructions contained in main memory  1204  causes processor  1202  to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in memory  1204 . In alternative aspects, hard-wired circuitry may be used in place of or in combination with software instructions to implement various aspects of the present disclosure. Thus, aspects of the present disclosure are not limited to any specific combination of hardware circuitry and software. 
     Various aspects of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical consumer interface or a Web browser through which a consumer can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. The communication network can include, for example, any one or more of a LAN, a WAN, the Internet, and the like. Further, the communication network can include, but is not limited to, for example, any one or more of the following network topologies, including a bus network, a star network, a ring network, a mesh network, a star-bus network, tree or hierarchical network, or the like. The communications modules can be, for example, modems or Ethernet cards. 
     Computer system  1200  can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Computer system  1200  can be, for example, and without limitation, a desktop computer, laptop computer, or tablet computer. Computer system  1200  can also be embedded in another device, for example, and without limitation, a mobile telephone, a PDA, a mobile audio player, a Global Positioning System (GPS) receiver, a video game console, and/or a television set top box. 
     The term “machine-readable storage medium” or “computer-readable medium” as used herein refers to any medium or media that participates in providing instructions to processor  1202  for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as data storage device  1206 . Volatile media include dynamic memory, such as memory  1204 . Transmission media include coaxial cables, copper wire, and fiber optics, including the wires forming bus  1208 . Common forms of machine-readable media include, for example, floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. The machine-readable storage medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or a combination of one or more of them. 
     To illustrate the interchangeability of hardware and software, items such as the various illustrative blocks, modules, components, methods, operations, instructions, and algorithms have been described generally in terms of their functionality. Whether such functionality is implemented as hardware, software, or a combination of hardware and software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. 
     As used herein, the phrase “at least one of” preceding a series of items, with the terms “and” or “or” to separate any of the items, modifies the list as a whole, rather than each member of the list (e.g., each item). The phrase “at least one of” does not require selection of at least one item; rather, the phrase allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items. By way of example, the phrases “at least one of A, B, and C” or “at least one of A, B, or C” each refer to only A, only B, or only C; any combination of A, B, and C; and/or at least one of each of A, B, and C. 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments. Phrases such as an aspect, the aspect, another aspect, some aspects, one or more aspects, an implementation, the implementation, another implementation, some implementations, one or more implementations, an embodiment, the embodiment, another embodiment, some embodiments, one or more embodiments, a configuration, the configuration, another configuration, some configurations, one or more configurations, the subject technology, the disclosure, the present disclosure, and other variations thereof and alike are for convenience and do not imply that a disclosure relating to such phrase(s) is essential to the subject technology or that such disclosure applies to all configurations of the subject technology. A disclosure relating to such phrase(s) may apply to all configurations, or one or more configurations. A disclosure relating to such phrase(s) may provide one or more examples. A phrase such as an aspect or some aspects may refer to one or more aspects and vice versa, and this applies similarly to other foregoing phrases. 
     A reference to an element in the singular is not intended to mean “one and only one” unless specifically stated, but rather “one or more.” The term “some” refers to one or more. Underlined and/or italicized headings and subheadings are used for convenience only, do not limit the subject technology, and are not referred to in connection with the interpretation of the description of the subject technology. Relational terms such as first and second and the like may be used to distinguish one entity or action from another without necessarily requiring or implying any actual such relationship or order between such entities or actions. All structural and functional equivalents to the elements of the various configurations described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and intended to be encompassed by the subject technology. Moreover, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is explicitly recited in the above description. No claim element is to be construed under the provisions of 35 U.S.C. § 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.” 
     While this specification contains many specifics, these should not be construed as limitations on the scope of what may be described, but rather as descriptions of particular implementations of the subject matter. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially described as such, one or more features from a described combination can in some cases be excised from the combination, and the described combination may be directed to a subcombination or variation of a subcombination. 
     The subject matter of this specification has been described in terms of particular aspects, but other aspects can be implemented and are within the scope of the following claims. For example, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. The actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the aspects described above should not be understood as requiring such separation in all aspects, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     The title, background, brief description of the drawings, abstract, and drawings are hereby incorporated into the disclosure and are provided as illustrative examples of the disclosure, not as restrictive descriptions. It is submitted with the understanding that they will not be used to limit the scope or meaning of the claims. In addition, in the detailed description, it can be seen that the description provides illustrative examples and the various features are grouped together in various implementations for the purpose of streamlining the disclosure. The method of disclosure is not to be interpreted as reflecting an intention that the described subject matter requires more features than are expressly recited in each claim. Rather, as the claims reflect, inventive subject matter lies in less than all features of a single disclosed configuration or operation. The claims are hereby incorporated into the detailed description, with each claim standing on its own as a separately described subject matter. 
     The claims are not intended to be limited to the aspects described herein, but are to be accorded the full scope consistent with the language claims and to encompass all legal equivalents. Notwithstanding, none of the claims are intended to embrace subject matter that fails to satisfy the requirements of the applicable patent law, nor should they be interpreted in such a way.