Patent Description:
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's face or head, such as to portray the accurate depth and distance of the user's face or head within the device. Documents <CIT> and <CIT> disclose near-eye display devices including outward facing displays.

In accordance with a first aspect of the present disclosure, there is provided a device, comprising: a near-eye display configured to provide an image to a subject; an eye imaging system configured to collect an image of the subject; and a light field display configured to provide an autostereoscopic image of a three-dimensional model of the subject to an onlooker, wherein the autostereoscopic image includes a perspective-corrected view of the subject from multiple viewpoints within a field of view of the light field display.

In some embodiments, the light field display comprises a pixel array and a multi-lenslet array, wherein the pixel array is configured to provide a segmented view of the subject to the multi-lenslet array, the segmented view including multiple portions of the field of view of the light field display at a selected viewpoint.

In some embodiments, the eye imaging system comprises two cameras to collect a binocular view of the subject.

In some embodiments, the device further comprises one or more processors and a memory storing instructions which, when executed by the one or more processors, generate a three dimensional representation of the subject from the image of the subject.

In some embodiments, the near-eye display provides the subject a three-dimensional representation of an environment, including the onlooker.

In some embodiments, the eye imaging system includes an infrared camera that receives the image from the subject in a reflective mode from a dichroic mirror adjacent to the light field display.

In some embodiments, the light field display comprises a micro lens array having multiple micro lenses arranged in a two-dimensional pattern having a pre-selected pitch to avoid cross-talk between the perspective-corrected view for two viewpoints, for the onlooker.

In some embodiments, the light field display further comprises an immersed stop adjacent to a micro lens array, the immersed stop including multiple apertures such that each aperture is aligned with a center of each micro lens in the micro lens array.

In some embodiments, wherein the light field display includes a pixel array split in multiple active segments, wherein each active segment in the pixel array has a dimension corresponding to a diameter of a refractive element in a multi-lenslet array.

In some embodiments, the device further comprises one or more processors and a memory storing instructions which, when executed by the one or more processors, cause the light field display to split a pixel array into multiple active segments, each active segment configured to provide a portion of the field of view of the light field display at a selected viewpoint for the onlooker.

In accordance with a second aspect of the present disclosure, there is provided a computer-implemented method, comprising: receiving, from one or more headset cameras, multiple images having at least two or more fields of view of a subject, wherein the subject is a headset user; extracting multiple image features from the images using a set of learnable weights; forming a three-dimensional model of the subject using the set of learnable weights; mapping the three-dimensional model of the subject onto an autostereoscopic display format that associates an image projection of the subject with a selected observation point for an onlooker; and providing, on a device display, the image projection of the subject when the onlooker is located at the selected observation point.

In some embodiments, extracting image features comprises extracting intrinsic properties of a headset camera used to collect each of the images.

In some embodiments, mapping the three-dimensional model of the subject onto an autostereoscopic display format comprises interpolating a feature map associated with a first observation point with a feature map associated with a second observation point.

In some embodiments, mapping the three-dimensional model of the subject onto an autostereoscopic display format comprises aggregating the image features for multiple pixels along a direction of the selected observation point.

In some embodiments, mapping the three-dimensional model of the subject onto an autostereoscopic display format comprises concatenating multiple feature maps produced by each of the headset cameras in a permutation invariant combination, each of the headset cameras having an intrinsic characteristic.

In some embodiments, providing the image projection of the subject comprises providing, on the device display, a second image projection as the onlooker moves from a first observation point to a second observation point.

In accordance with a third aspect of the present disclosure, there is provide a computer-implemented method for training a model to provide a view of a subject to an auto stereoscopic display in a virtual reality headset, comprising: collecting, from a face of multiple users, multiple ground-truth images; rectifying the ground-truth images with stored, calibrated stereoscopic pairs of images; mapping a three-dimensional model of the subject onto an autostereoscopic display format that associates an image projection of the subject with a selected observation point for an onlooker; determining a loss value based on a difference between the ground-truth images and the image projection of the subject; and updating the three-dimensional model of the subject based on the loss value.

In some embodiments, generating multiple synthetic views comprises projecting image features from each of the ground-truth images along a selected observation direction and concatenating multiple feature maps produced by each of the ground-truth images in a permutation invariant combination, each of the ground-truth images having an intrinsic characteristic.

In some embodiments, training the three-dimensional model of the subject comprises updating at least one in a set of learnable weights for each of multiple features based on a value of a loss function indicative of the difference between the ground-truth images and the image projection of the subject.

In some embodiments, training the three-dimensional model of the subject comprises training a background value for each of multiple pixels in the ground-truth images based on a pixel background value projected from the multiple ground-truth images.

In accordance with a fourth aspect of the present disclosure, there is provided a system includes a first means for storing instructions and a second means for executing the instructions to perform a method, the method includes receiving multiple two-dimensional images having at least two or more fields of view of a subject, extracting multiple image features from the two-dimensional images using a set of learnable weights, projecting the image features along a direction between a three-dimensional model of the subject and a selected observation point for an onlooker, and providing, to the onlooker, an autostereoscopic image of the three-dimensional model of the subject.

It will be appreciated that any features described herein as being suitable for incorporation into one or more aspects or embodiments of the present disclosure are intended to be generalizable across any and all aspects and embodiments of the present disclosure. The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims.

In the figures, like elements are labeled likewise, according to their description, unless explicitly stated otherwise.

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'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> illustrates a headset 10A including autostereoscopic external displays 110A, according to some embodiments. Headset 10A may be an AR or VR device configured to be mounted on a user's head. Headset 10A includes two eyepieces 100A mechanically coupled by a strap <NUM> and having a flexible mount to hold electronics components <NUM> in the back of the user's head. A flex connector <NUM> may electronically couple eyepieces 100A with electronic components <NUM>. Each of eyepieces 100A include eye imaging systems <NUM>-<NUM> and <NUM>-<NUM> (hereinafter, collectively referred to as "eye imaging systems <NUM>"), 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 <NUM> 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's face. Eye imaging systems <NUM> may provide information about pupil location and movement to the electronics components. Eyepieces 100A may also include external displays 110A (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 <NUM> may include a memory circuit <NUM> storing instructions and a processor circuit <NUM> that executes the instructions to receive the image of the portion of the face of the user from eye imaging systems <NUM>, and provide to external displays 110A the autostereoscopic image of the face of the user. Moreover, electronics components <NUM> may also receive the image from the portion of the user'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 <NUM> include a communications module <NUM> configured to communicate with a network. Communications module <NUM> may include radio-frequency software and hardware to wirelessly communicate memory <NUM> and processor <NUM> with an external network, or some other device. Accordingly, communications module <NUM> 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 <NUM> may also communicate with other input tools and accessories cooperating with headset 10A (e.g., handle sticks, joysticks, mouse, wireless pointers, and the like).

In some embodiments, eyepieces 100A may include one or more exterior cameras <NUM>-<NUM> and <NUM>-<NUM> (hereinafter, collectively referred to as "exterior cameras <NUM>") to capture a front view of a scene for the user. In some embodiments, exterior cameras <NUM> may focus or be directed to (e.g., by processor <NUM>) aspects of the front view that the user may be particularly interested in, based on the gaze, vergence, and other features of the user's view that may be derived from the image of the portion of the user's face provided by the dual eye camera.

<FIG> illustrates a headset 10B as viewed by a forward onlooker, according to some embodiments. In some embodiments, headset 10B may be an AR or VR device in a "snorkel" configuration. Hereinafter, headsets 10A and 10B will be collectively referred to as "headsets <NUM>. " In some embodiments, a visor 100B may include a single forward display 110B that provides a view of user <NUM> to an onlooker <NUM>. Display 110B includes a portion of the face having the two eyes, a portion of the nose, eyebrows, and other facial features of user <NUM>. Further, an autostereoscopic image <NUM> of the user's face may include details such as an accurate and real-time position of the user's eyes, indicating a gaze direction and a vergence or focus of attention of user <NUM>. This may indicate to onlooker <NUM> 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's attention.

In some embodiments, autostereoscopic image <NUM> offers a 3D rendering of the face of the user. Accordingly, onlooker <NUM> has a full body view of the user's face and even the user's head, changing perspective as onlooker <NUM> changes an angle of view. In some embodiments, the outwardly projected display 110B may include image features additional to the image of a portion of the user'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'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> illustrates a detailed view of an eyepiece <NUM> for an AR or VR device configured to provide a reverse pass-through view of the user's face to a forward onlooker (cf. eyepieces 100A and snorkel visor 100B), according to some embodiments. Eyepiece <NUM> includes an optical surface <NUM> configured to provide an image to a user on a first side (to the left) of optical surface <NUM>. In some embodiments, the image to the user may be provided by a forward camera <NUM>, and optical surface <NUM> may include a display coupled to forward camera <NUM>. In some embodiments, the image in optical surface <NUM> may be a virtual image provided by a processor executing instructions stored in a memory (e.g., for VR devices, memory <NUM> and processor <NUM>). 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 <NUM> via transparent optical components (e.g., lenses, waveguides, prisms, and the like).

In some embodiments, eyepiece <NUM> also includes a first eye camera 215A and a second eye camera 215B (hereinafter, collectively referred to as "eye cameras <NUM>") configured to collect first and second images of the user's face (e.g., the eye of the user) at two different FOVs. In some embodiments, eye cameras <NUM> may be infrared cameras collecting images of the user's face in reflection mode, from a hot mirror assembly <NUM>. An illumination ring <NUM> may provide illumination to the portion of the user's face that is going to be imaged by eye cameras <NUM>. Accordingly, optical surface <NUM> may be configured to be reflective at the wavelength of light operated by eye cameras <NUM> (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 210B projects an autostereoscopic image of the face of the user to an onlooker (to the right end of the figure).

<FIG> illustrate different aspects and components of a micro lens array <NUM> 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 <NUM> receives light from a pixel array <NUM> 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> is a detailed view of micro lens array <NUM> and includes multiple micro lenses <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM> (collectively referred to, hereinafter, as "micro lenses <NUM>") arranged in a two-dimensional pattern <NUM> having a pitch <NUM>. In some embodiments, an aperture mask <NUM> may be disposed adjacent to the micro lens array such that one aperture is aligned with each micro lens <NUM>, to avoid cross-talk of different angles of view from the point of view of the onlooker.

For illustrative purposes only, pattern <NUM> is a hexagonal lattice of micro lenses <NUM> having a pitch <NUM> of less than a millimeter (e.g., <NUM>). Micro lens array <NUM> may include a first surface and a second surface <NUM> including concavities forming micro lenses <NUM>, the first and second surfaces <NUM> separated by a transmissive substrate <NUM> (e.g., N-BK7 glass, plastic, and the like). In some embodiments, transmissive substrate <NUM> may have a thickness of about <NUM>.

<FIG> is a detailed view of a light field display <NUM> for use in a reverse pass-through headset, according to some embodiments. Light field display <NUM> includes a pixel array <NUM> adjacent to a micro lens array (e.g., micro lens array <NUM>), of which only a micro lens <NUM> is shown, for illustrative purposes. Pixel array <NUM> includes multiple pixels <NUM> generating light beams <NUM> directed to micro lens <NUM>. In some embodiments, a distance <NUM> between pixel array <NUM> and micro lens <NUM> may be approximately equal to the focal length of micro lens <NUM>, and therefore outgoing light beams <NUM> may be collimated in different directions, depending on the specific position of the originating pixel <NUM>. Accordingly, different pixels <NUM> in pixel array <NUM> may provide a different angle of view of a 3D representation of the user's face, depending on the location of the onlooker.

<FIG> is a plan view of micro lens array <NUM>, showing a honeycomb pattern.

<FIG> illustrates micro lens array <NUM> with aperture mask <NUM> disposed adjacent to it so that openings on aperture mask <NUM> are centered on micro lens array <NUM>. In some embodiments, aperture mask <NUM> may include chrome, having apertures of about <NUM> over a <NUM> hex-pack pitch (as illustrated). Aperture mask <NUM> may be lined up with the first surface or the second surface <NUM>, on either side of micro lens array <NUM>, or on both sides.

<FIG> illustrates a ray-tracing view of a light field display <NUM> 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 <NUM> includes a micro lens array <NUM> 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 <NUM> includes multiple micro-lenses <NUM> arranged in a two-dimensional pattern, as disclosed herein. A pixel array <NUM> may include multiple pixels <NUM> providing light rays <NUM> that are transmitted through micro lens array <NUM> 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 <NUM> may include aperture mask <NUM>. Aperture mask <NUM> provides blocking elements near the edges of each of the micro lenses in micro lens array <NUM>. The blocking elements reduce the amount of light rays 425B and 425C relative to light rays 425A forming the front view of the user'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's face (down, according to <FIG>).

<FIG> illustrate different aspects of a resolution power characteristic 500A, 500B, and 500C (hereinafter, collectively referred to as "resolution power characteristics <NUM>") 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 <NUM> (X-axis) in resolution power characteristics <NUM> indicates an image distance (in mm) between the user's face (e.g., the eye of the user) and the micro lens array. The ordinates <NUM> (Y-axis) in resolution power characteristics <NUM> 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> illustrates resolution power characteristic 500A including a cutoff value, which is the highest frequency that the onlooker may distinguish from the display. Curves <NUM>-1A and <NUM>-2A (hereinafter, collectively referred to as "curves 501A") are associated with two different headset models (referred to as Model <NUM> and Model <NUM>, 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 <NUM>). 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 <NUM>). This is illustrated by the difference in cutoff values <NUM>-2A (approx. <NUM> cycles/mm) for curve <NUM>-2A, and <NUM>-1A (approx. <NUM> cycles/mm) for curve <NUM>-1A. Indeed, the headset model for curve <NUM>-2A has a larger image distance (close to <NUM> between user face and display) than the headset model for curve <NUM>-1A (about <NUM> between user eyes and display). Also, for a micro lens array having a wider pitch (Model <NUM>, <NUM> pitch), the resolution cutoff will be reduced relative to a smaller pitch (Model <NUM>, <NUM> pitch).

<FIG> illustrates resolution power characteristic 500B including a curve 501B for a light field display model (Model <NUM>) providing a spatial frequency of about <NUM> cycles/mm with an image distance of about <NUM> at point 510B.

<FIG> illustrates resolution power characteristic 500C including curves <NUM>-1C, <NUM>-2C, <NUM>-3C, and <NUM>-4C (hereinafter, collectively referred to as "curves 501C"). The abscissae 521C (X-axis) for resolution power characteristics 500C indicates a headset depth (e.g., similar to a distance between the user's eyes/face and the light field display), and the ordinates 522C (Y-axis) indicate a pixel pitch (in microns, µm) for the pixel array in the light field display. Each one of curves 501C indicates a number of cycles/mm cutoff resolution for each light field display model. Point 510B is illustrated in comparison with a better resolution obtained at point 510C for a light field display model (Model <NUM>) with high density pixel packing (less than <NUM> pitch), and a close headset depth of about <NUM> (e.g., about one inch or less).

<FIG> illustrates images <NUM>-1D and <NUM>-2D of the user wearing the headset according to an onlooker, for each of the light field display models. Image <NUM>-1D is obtained with Model <NUM>, and image <NUM>-2D is obtained with Model <NUM>, of the light field display (cf. points 510B and 510C, respectively). The resolution performance of Model <NUM> is certainly better than that of Model <NUM>, 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> illustrates a 3D rendition 621A and 621B (hereinafter, collectively referred to as "3D rendition <NUM>") of a portion of a face of a user of an AR or VR device, according to some embodiments. In some embodiments, 3D rendition <NUM> is provided by a model <NUM> operating on multiple 2D images <NUM> of at least a portion of the user's face (e.g., the eyes), and provided by an eye imaging system in the AR or VR device (cf. eye imaging system <NUM> and eye cameras <NUM>). Model <NUM> 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 <NUM> 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 <NUM> and processor <NUM> in electronics components <NUM>). Accordingly, multiple 2D images <NUM> are received from the eye imaging system to create, update, and improve model <NUM>. 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 <NUM> can determine which image came from which camera, to form 3D rendition <NUM>. Model <NUM> 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 <NUM> of at least a portion of the face of the user of the AR or VR device.

<FIG> illustrates a block diagram of a model architecture <NUM> used for a 3D rendition of a face portion of a VR/AR headset user, according to some embodiments. Model architecture <NUM> is a pixel aligned volumetric avatar (PVA) model. PVA model <NUM> is learned from a multi-view image collection that produces multiple, 2D input images <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-n (hereinafter, collectively referred to as "input images <NUM>). Each of input images <NUM> is associated with a camera view vector, vi (e.g., v<NUM>, v<NUM> and vn), which indicates the direction of view of the user's face for that particular image. Each of vectors vi is a known viewpoint <NUM>, associated with camera intrinsic parameters, Ki, and rotation, Ri (e.g., {Ki, [R|t]i}). Camera intrinsic parameters Ki may include brightness, color mapping, sensor efficiency, and other camera-dependent parameters. Rotation, Ri, indicates the orientation (and distance) of the subject's head relative to the camera. The different camera sensors have a slightly different response to the same incident radiance despite the fact that they are the same camera model. If nothing is done to address this, the intensity differences end up baked into the scene representation N, which will cause the image to unnaturally brighten or darken from certain viewpoints. To address this, we learn a per-camera bias and gain value. This allows the system to have an 'easier' way to explain this variation in the data.

The value of 'n' is purely exemplary, as anyone with ordinary skills would realize that any number, n, of input images <NUM> can be used. PVA model <NUM> produces a volumetric rendition <NUM> of the headset user. Volumetric rendition <NUM> is a 3D model (e.g., "avatar") that can be used to generate a 2D image of the subject from the target viewpoint. This 2D image changes as the target viewpoint changes (e.g., as the onlooker moves around the headset user).

PVA model <NUM> includes a convolutional encoder-decoder 710A, a ray marching stage 710B, and a radiance field stage 710C (hereinafter, collectively referred to as "PVA stages <NUM>"). PVA model <NUM> is trained with input images <NUM> selected from a multi-identity training corpus, using gradient descent. Accordingly, PVA model <NUM> includes a loss function defined between predicted images from multiple subjects and the corresponding ground truth. This enables PVA model <NUM> to render accurate volumetric renditions <NUM> independently of the subject.

Convolutional encoder-decoder network 710A takes input images <NUM> and produces pixel-aligned feature maps <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-n (hereinafter, collectively referred to as "feature maps <NUM>"). Ray marching stage 710B follows each of the pixels along a ray in target view j, defined by {Kj , [R|t]j}, accumulating color, c, and optical density ("opaqueness") produced by radiance field stage 710C at each point. Radiance field stage 710C (N) converts 3D location and pixel-aligned features to color and opacity, to render a radiance field <NUM> (c, σ).

Input images <NUM> are 3D objects having a height (h) and a width (w) corresponding to the 2D image collected by a camera along direction vi, and a depth of <NUM> layers for each color pixel R, G, B. Feature maps <NUM> are 3D objects having dimensions h ×w × d. Encoder-decoder network 710A encodes input images <NUM> using learnable weights <NUM>-<NUM>, <NUM>-<NUM>. <NUM>-n (hereinafter, collectively referred to as "learnable weights <NUM>"). Ray marching stage 710B performs world to camera projections <NUM>, bilinear interpolations <NUM>, positional encoding <NUM>, and feature aggregation <NUM>.

In some embodiments, for a conditioning view vi ∈ R h×w×<NUM> feature maps <NUM> may be defined as functions <MAT>
where ϕ(X): R<NUM> → R <NUM>×<NUM> is the positional encoding of a point <NUM> (X ∈ R <NUM>) with <NUM> × l different basis functions. Point <NUM> (X), is a point along a ray directed from a 2D image of the subject to a specific viewpoint <NUM>, r<NUM>. Feature maps <NUM> (f(i) ∈ Rh×w×d) are associated with a camera position vector, vi, where d is the number of feature channels, h and w are image height and width, and fX ∈ Rd' is an aggregated image feature associated with point X. For each feature map f(i), ray marching stage 710B obtains fX ∈ Rd by projecting 3D point X along the ray using camera intrinsic (K) and extrinsic (R, t) parameters of that particular viewpoint, <MAT> <MAT>
where Π is a perspective projection function to camera pixel coordinates, and F(f, x) is the bilinear interpolation <NUM> of f at pixel location x. Ray marching stage 710B combines pixel-aligned features f(i)X from multiple images for radiance field stage 710C.

For each given training image vj with camera intrinsics Kj and rotation and translation Rj, tj, the predicted color of a pixel p ∈ R<NUM> for a given viewpoint in the focal plane of the camera and center <NUM> (r<NUM>) ∈ R<NUM> is obtained by marching rays into the scene using the camera-to-world projection matrix, P-<NUM> = [Ri|ti]-<NUM>K-<NUM>i with the direction of the rays given by, <MAT>.

Ray marching stage 710B accumulates radiance and opacity values along a ray <NUM> defined by r(t) = r<NUM> + td for t ∈ [tnear, tfar] as follows: <MAT>.

In some embodiments, ray marching stage 710B uniformly samples a set of ns points t ~[tnear, tfar]. Setting X = r(t) the quadrature rule may be used to approximate integrals <NUM> and <NUM>. A function Iα(p) may be defined as <MAT>
where αi= <NUM> - exp(-δi·σi) with δi being the distance between the i+<NUM>-th and i-th sample point along ray <NUM>.

In a multi-view setting with known camera viewpoints, vi, and a fixed number of conditioning views ray marching stage 710B aggregates the features by simple concatenation. Concretely, for n conditioning images {vi}ni=<NUM> with corresponding rotation and translation matrices given by {Ri}ni=<NUM> and {ti}ni=<NUM>, using features {f(i)x }ni=<NUM> for each point X as in Eq. (<NUM>), ray marching stage 710B generates the final feature as follows, <MAT>.

Where ⊕ represents a concatenation along the depth dimension. This preserves feature information from viewpoints, {vi}ni=<NUM>, helping PVA model <NUM> to determine the best combination and employ the conditioning information.

In some embodiments, PVA model <NUM> is agnostic to viewpoint and number of conditioning views. Simple concatenation as above is insufficient in this case, since the number of conditioning views may not be known a priori, leading to different feature dimensions (d) during inference time. To summarize features for a multi-view setting, some embodiments include a permutation invariant function G: Rn×d → Rd such that for any permutation ψ, <MAT>.

A simple permutation invariant function for feature aggregation is the mean of the sampled feature maps <NUM>. This aggregation procedure may be desirable when depth information during training is available. However, in the presence of depth ambiguity (e.g., for points that are projected onto feature map <NUM> before sampling), the above aggregation may lead to artifacts. To avoid this, some embodiments consider camera information to include effective conditioning in radiant field stage 710C. Accordingly, some embodiments include a conditioning function network Ncf: Rd+<NUM> → Rd' that takes the feature vector, f(i)X, and the camera information (ci) and produces a camera summarized feature vector f'(i)X. These modified vectors are then averaged over multiple, or all, conditioning views, as follows <MAT> <MAT>.

The advantage of this approach is that the camera summarized features can take likely occlusions into account before the feature average is performed. The camera information is encoded as a 4D rotation quaternion and a 3D camera position.

Some embodiments may also include a background estimation network, Nbg, to avoid learning parts of the background in the scene representation. Background estimation network, Nbg, may be defined as: Nbg: Rnc:→ Rh×w×<NUM> to learn a per-camera fixed background. In some embodiments, radiant field stage 710C may use Nbg to predict the final image pixels as: <MAT>
with Ibg =Ibg +Nbg(ci) for camera ci where Ibg is an initial estimate of the background extracted using inpainting, and Iα is as defined by Eq. (<NUM>). These inpainted backgrounds are often noisy leading to 'halo' effects around the head of the person. To avoid this, Nbg model learns the residual to the inpainted background. This has the advantage of not needing a high capacity network to account for the background.

For ground truth target images vj, PVA model <NUM> trains both radiance field stage 710C and feature extraction network using a simple photo-metric reconstruction loss: <MAT>.

<FIG> illustrate elements and steps in a method for training a model to provide a view of a portion of a user's face to an autostereoscopic display in a virtual reality headset, according to some embodiments. An eyepiece <NUM> is trained with multiple training images <NUM> from multiple users. A 3D model <NUM> for each of the users is created including a texture map and a depth map to recover fine details of the image features <NUM>-1B, <NUM>-2B, and 833C (hereinafter, collectively referred to as "texture and depth maps <NUM>"). When 3D model <NUM> is generated, an autostereoscopic image of the three-dimensional reconstruction of the user'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 <NUM> in a selected angle of view for the onlooker.

<FIG> illustrates a setup <NUM> for collecting multiple training images <NUM> onto eyepiece <NUM>, according to some embodiments. Training images <NUM> may be provided by a display, and projected onto a screen <NUM> disposed at the same location as the hot mirror will be when the eyepiece is assembled in the headset. One or more infrared cameras <NUM> collect training images <NUM> in reflection mode, and one or more RGB cameras <NUM> collect training images in transmission mode. Setup <NUM> has an image vector <NUM>-<NUM>, an IR camera vector <NUM>-<NUM>, and an RGB camera vector <NUM>-<NUM> (hereinafter, collectively referred to as "positioning vectors <NUM>"), fixed for all training images <NUM>. Positioning vectors <NUM> are used by the algorithm models to accurately assess sizes, distances, and angles of view associated with 3D model <NUM>.

<FIG> illustrates texture and depth images <NUM>-1B and <NUM>-2B, according to some embodiments. Texture image <NUM>-1B may be obtained from a capture of a training image using RGB camera <NUM>, and depth image <NUM>-2B may be obtained from a training image using IR camera <NUM>.

<FIG> illustrates a depth image 833C collected with IR camera <NUM>, according to some embodiments. <FIG> illustrates 3D model <NUM> formed in relation to eyepiece <NUM>, according to some embodiments.

<FIG> illustrates a flowchart in a method <NUM> for providing an autostereoscopic view of a face of a VR/AR headset user, according to some embodiments. Steps in method <NUM> 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 <NUM>, processor <NUM>, electronics components <NUM>, and headsets <NUM>). In yet other embodiments, at least one or more of the steps in a method consistent with method <NUM> 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 <NUM>). In some embodiments, method <NUM> 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 <NUM>, model architecture <NUM>). In some embodiments, methods consistent with the present disclosure may include at least one or more steps from method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes receiving, from one or more headset cameras, multiple images having at least two or more fields of view of a subject, wherein the subject is a headset user.

Step <NUM> includes extracting multiple image features from images using a set of learnable weights. In some embodiments, step <NUM> 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 <NUM> 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 <NUM> includes generating a texture map of the portion of the user's face and a depth map of the portion of the user'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. In some embodiments, step <NUM> includes extracting intrinsic properties of a headset camera used to collect each of the images.

Step <NUM> includes forming a three-dimensional model of the subject using the learnable weights.

Step <NUM> includes mapping the three-dimensional model of the subject onto an autostereoscopic display format that associates an image projection of the subject with a selected observation point for an onlooker. In some embodiments, step <NUM> includes providing, to one segment of a light field display, a portion of a field of view of the user's face at a selected viewpoint for the onlooker. In some embodiments, step <NUM> 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. In some embodiments, step <NUM> includes interpolating a feature map associated with a first observation point with a feature map associated with a second observation point. In some embodiments, step <NUM> includes aggregating the image features for multiple pixels along a direction of the selected observation point. In some embodiments, step <NUM> includes concatenating multiple feature maps produced by each of the headset cameras in a permutation invariant combination, each of the headset cameras having an intrinsic characteristic.

Step <NUM> includes providing, on the display, the image projection of the subject when the onlooker is located at the selected observation point. In some embodiments, step <NUM> includes providing, on the device display, a second image projection as the onlooker moves from a first observation point to a second observation point.

<FIG> illustrates a flowchart in a method <NUM> for rendering a three-dimensional (3D) view of a portion of a user's face from multiple, two-dimensional (2D) images of a portion of the user's face. Steps in method <NUM> 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 <NUM>, processor <NUM>, electronics components <NUM>, and headsets <NUM>). In yet other embodiments, at least one or more of the steps in a method consistent with method <NUM> 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 <NUM>). In some embodiments, method <NUM> 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 <NUM>, model architecture <NUM>). In some embodiments, methods consistent with the present disclosure may include at least one or more steps from method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes collecting, from a face of multiple users, multiple ground-truth images.

Step <NUM> includes rectifying the ground-truth images with stored, calibrated stereoscopic pairs of images. In some embodiments, step <NUM> includes extracting multiple image features from the two-dimensional images using a set of learnable weights. In some embodiments, step <NUM> includes extracting intrinsic properties of a camera used to collect the two-dimensional images.

Step <NUM> includes mapping the three-dimensional model of the subject onto an autostereoscopic display format that associates an image projection of the subject with a selected observation point for an onlooker. In some embodiments, step <NUM> includes projecting the image features along a direction between a three-dimensional model of the subject and a selected observation point for an onlooker. In some embodiments, step <NUM> includes interpolating a feature map associated with a first direction with a feature map associated with a second direction. In some embodiments, step <NUM> includes aggregating the image features for multiple pixels along the direction between the three-dimensional model of the subject and the selected observation point. In some embodiments, step <NUM> includes concatenating multiple feature maps produced by each of multiple cameras in a permutation invariant combination, each of the multiple cameras having an intrinsic characteristic.

Step <NUM> includes determining a loss value based on a difference between the ground-truth images and the image projection of the subject. In some embodiments, step <NUM> includes providing, to the onlooker, an autostereoscopic image of the three-dimensional model of the subject. In some embodiments, step <NUM> includes evaluating a loss function based on a difference between the autostereoscopic image of the three-dimensional model of the subject and a ground truth image of the subject, and updating at least one of the set of learnable weights based on the loss function.

Step <NUM> includes updating the three-dimensional model of the subject based on the loss value.

<FIG> illustrates a flowchart in a method <NUM> for training a model to render a three-dimensional (3D) view of a portion of a user's face from multiple, two-dimensional (2D) images of a portion of the user's face, according to some embodiments. Steps in method <NUM> 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 <NUM>, processor <NUM>, electronics components <NUM>, and headsets <NUM>). In yet other embodiments, at least one or more of the steps in a method consistent with method <NUM> 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 <NUM>). In some embodiments, method <NUM> 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 <NUM>, model architecture <NUM>). In some embodiments, methods consistent with the present disclosure may include at least one or more steps from method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes rectifying the ground-truth images with stored, calibrated stereoscopic pairs of images.

Step <NUM> includes generating, with a three-dimensional face model, multiple synthetic views of subjects, wherein the synthetic views of subjects include an interpolation of multiple feature maps projected along different directions corresponding to multiple views of the subjects. In some embodiments, step <NUM> includes projecting image features from each of the ground-truth images along a selected observation direction and concatenating multiple feature maps produced by each of the ground-truth images in a permutation invariant combination, each of the ground-truth images having an intrinsic characteristic.

Step <NUM> includes training the three-dimensional face model based on a difference between the ground-truth images and the synthetic views of subjects. In some embodiments, step <NUM> includes updating at least one in a set of learnable weights for each of multiple features in the feature maps based on a value of a loss function indicative of the difference between the ground-truth images and the synthetic views of subjects. In some embodiments, step <NUM> includes training a background value for each of multiple pixels in the ground-truth images based on a pixel background value projected from the multiple ground-truth images.

<FIG> is a block diagram illustrating an exemplary computer system <NUM> with which headsets <NUM>, and methods <NUM>, <NUM>, and <NUM> can be implemented. In certain aspects, computer system <NUM> 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 <NUM> 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 <NUM> includes a bus <NUM> or other communication mechanism for communicating information, and a processor <NUM> (e.g., processor <NUM>) coupled with bus <NUM> for processing information. By way of example, the computer system <NUM> may be implemented with one or more processors <NUM>. Processor <NUM> 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 <NUM> 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 <NUM> (e.g., memory <NUM>), 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 <NUM> for storing information and instructions to be executed by processor <NUM>. The processor <NUM> and the memory <NUM> can be supplemented by, or incorporated in, special purpose logic circuitry.

The instructions may be stored in the memory <NUM> 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 <NUM>, 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, wirth languages, and xml-based languages. Memory <NUM> may also be used for storing temporary variable or other intermediate information during execution of instructions to be executed by processor <NUM>.

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).

Computer system <NUM> further includes a data storage device <NUM> such as a magnetic disk or optical disk, coupled with bus <NUM> for storing information and instructions. Computer system <NUM> may be coupled via input/output module <NUM> to various devices. Input/output module <NUM> can be any input/output module. Exemplary input/output modules <NUM> include data ports such as USB ports. The input/output module <NUM> is configured to connect to a communications module <NUM>. Exemplary communications modules <NUM> include networking interface cards, such as Ethernet cards and modems. In certain aspects, input/output module <NUM> is configured to connect to a plurality of devices, such as an input device <NUM> and/or an output device <NUM>. Exemplary input devices <NUM> 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 <NUM>. Other kinds of input devices <NUM> 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 <NUM> 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 <NUM> can be implemented, at least partially, using a computer system <NUM> in response to processor <NUM> executing one or more sequences of one or more instructions contained in memory <NUM>. Such instructions may be read into memory <NUM> from another machine-readable medium, such as data storage device <NUM>. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in memory <NUM>. 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 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 <NUM> can include clients and servers. Computer system <NUM> can be, for example, and without limitation, a desktop computer, laptop computer, or tablet computer. Computer system <NUM> 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 <NUM> 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 <NUM>. Volatile media include dynamic memory, such as memory <NUM>. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires forming bus <NUM>. 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.

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).

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 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. 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 encompassed by the subject technology.

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. 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.

Claim 1:
A device, comprising:
a near-eye display configured to provide an image to a subject;
an eye imaging system configured to collect an image of the subject; and
a light field display configured to provide an autostereoscopic image of a three-dimensional model of the subject to an onlooker, wherein the autostereoscopic image includes a perspective-corrected view of the subject from multiple viewpoints within a field of view of the light field display.