Facebook Patent | Vergence Determination

Patent: Vergence Determination

Publication Number: 20200089316

Publication Date: 20200319

Applicants: Facebook

Abstract

In one embodiment, the artificial reality system determines that a performance metric of an eye tracking system is below a first performance threshold. The eye tracking system is associated with a head-mounted display worn by a user. The artificial reality system receives first inputs associated with the body of a user and determines a region that the user is looking at within a field of view of a head-mounted display based on the received first inputs. The system determines a vergence distance of the user based at least on the first inputs associated with the body of the user, the region that the user is looking at, and locations of one or more objects in a scene displayed by the head-mounted display. The system adjusts one or more configurations of the head-mounted display based on the determined vergence distance of the user.

TECHNICAL FIELD

[0001] This disclosure generally relates to artificial reality, such as virtual reality and augmented reality.

BACKGROUND

[0002] Artificial reality is a form of reality that has been adjusted in some manner before presentation to a user, which may include, e.g., a virtual reality (VR), an augmented reality (AR), a mixed reality (MR), a hybrid reality, or some combination and/or derivatives thereof. Artificial reality content may include completely generated content or generated content combined with captured content (e.g., real-world photographs). The artificial reality content may include video, audio, haptic feedback, or some combination thereof, and any of which may be presented in a single channel or in multiple channels (such as stereo video that produces a three-dimensional effect to the viewer). Artificial reality may be associated with applications, products, accessories, services, or some combination thereof, that are, e.g., used to create content in an artificial reality and/or used in (e.g., perform activities in) an artificial reality. The artificial reality system that provides the artificial reality content may be implemented on various platforms, including a head-mounted display (HMD) connected to a host computer system, a standalone HMD, a mobile device or computing system, or any other hardware platform capable of providing artificial reality content to one or more viewers.

SUMMARY OF PARTICULAR EMBODIMENTS

[0003] Particular embodiments described herein relate to a method of determining vergence of a user using a combination of eye tracking based approaches (e.g., 3D eye tracking, machine learning based eye tracking), body-based approaches (e.g., head position/movement, hand position/movement, body position/movement) and content-based approaches (e.g., Z-buffer, face detection, application-developer provided information). Particular embodiments detect malfunction of an eye tracking system (e.g., data being out of range or no data from eye tracking system at all) and, upon detection of malfunction, approximate the user vergence using a combination of the approaches. In particular embodiments, a fusion algorithm weights the inputs from all these approaches and determine where the user is likely looking at (e.g., using a piecewise comparison). For example, when the headset detects that the user’s hand has picked up a virtual object and is moving toward his face, the fusion algorithm may infer that the user is looking at the virtual object in his hand. Upon identifying the virtual object as the likely subject of the user’s gaze, the system may determine an appropriate Z-depth for the display screen and adjust configurations of artificial reality system (e.g., changing a rendering image, moving a display screen, moving an optics block) accordingly to eliminate or ameliorate the negative effects caused by vergence accommodation conflict.

[0004] The embodiments disclosed herein are only examples, and the scope of this disclosure is not limited to them. Particular embodiments may include all, some, or none of the components, elements, features, functions, operations, or steps of the embodiments disclosed above. Embodiments according to the invention are in particular disclosed in the attached claims directed to a method, a storage medium, a system and a computer program product, wherein any feature mentioned in one claim category, e.g. method, can be claimed in another claim category, e.g. system, as well. The dependencies or references back in the attached claims are chosen for formal reasons only. However, any subject matter resulting from a deliberate reference back to any previous claims (in particular multiple dependencies) can be claimed as well, so that any combination of claims and the features thereof are disclosed and can be claimed regardless of the dependencies chosen in the attached claims. The subj ect-matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims. Furthermore, any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0005] FIG. 1 illustrates an example network environment associated with a social-networking system.

[0006] FIG. 2 illustrates an example artificial reality system.

[0007] FIG. 3 illustrates an example situation for vergence accommodation conflict in a head-mounted display.

[0008] FIG. 4 illustrates an example 3D eye tracking system.

[0009] FIG. 5 illustrates an example head-mounted display having an adjustable display screen.

[0010] FIG. 6 illustrates an example performance evaluation chart with different body-based and content-based input combinations.

[0011] FIG. 7 illustrates an example scene in the field of view of the user wearing an artificial reality headset.

[0012] FIG. 8A illustrates an example fusion algorithm for determining display screen Z-depth and confidence score.

[0013] FIG. 8B illustrates an example fusion algorithm using piecewise comparisons on inputs.

[0014] FIG. 9 illustrates an example method for determining vergence distance of the user based on a combination of inputs.

[0015] FIG. 10 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

[0016] FIG. 1 illustrates an example network environment 100 associated with a social-networking system. Network environment 100 includes a user 101, a client system 130, a social-networking system 160, and a third-party system 170 connected to each other by a network 110. Although FIG. 1 illustrates a particular arrangement of user 101, client system 130, social-networking system 160, third-party system 170, and network 110, this disclosure contemplates any suitable arrangement of user 101, client system 130, social-networking system 160, third-party system 170, and network 110. As an example and not by way of limitation, two or more of client system 130, social-networking system 160, and third-party system 170 may be connected to each other directly, bypassing network 110. As another example, two or more of client system 130, social-networking system 160, and third-party system 170 may be physically or logically co-located with each other in whole or in part. Moreover, although FIG. 1 illustrates a particular number of users 101, client systems 130, social-networking systems 160, third-party systems 170, and networks 110, this disclosure contemplates any suitable number of users 101, client systems 130, social-networking systems 160, third-party systems 170, and networks 110. As an example and not by way of limitation, network environment 100 may include multiple users 101, client system 130, social-networking systems 160, third-party systems 170, and networks 110.

[0017] In particular embodiments, user 101 may be an individual (human user), an entity (e.g., an enterprise, business, or third-party application), or a group (e.g., of individuals or entities) that interacts or communicates with or over social-networking system 160. In particular embodiments, social-networking system 160 may be a network-addressable computing system hosting an online social network. Social-networking system 160 may generate, store, receive, and send social-networking data, such as, for example, user-profile data, concept-profile data, social-graph information, or other suitable data related to the online social network. Social-networking system 160 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, social-networking system 160 may include an authorization server (or other suitable component(s)) that allows users 101 to opt in to or opt out of having their actions logged by social-networking system 160 or shared with other systems (e.g., third-party systems 170), for example, by setting appropriate privacy settings. A privacy setting of a user may determine what information associated with the user may be logged, how information associated with the user may be logged, when information associated with the user may be logged, who may log information associated with the user, whom information associated with the user may be shared with, and for what purposes information associated with the user may be logged or shared. Authorization servers may be used to enforce one or more privacy settings of the users of social-networking system 30 through blocking, data hashing, anonymization, or other suitable techniques as appropriate. In particular embodiments, third-party system 170 may be a network-addressable computing system. Third-party system 170 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, one or more users 101 may use one or more client systems 130 to access, send data to, and receive data from social-networking system 160 or third-party system 170. Client system 130 may access social-networking system 160 or third-party system 170 directly, via network 110, or via a third-party system. As an example and not by way of limitation, client system 130 may access third-party system 170 via social-networking system 160. Client system 130 may be any suitable computing device, such as, for example, a personal computer, a laptop computer, a cellular telephone, a smartphone, a tablet computer, or an augmented/virtual reality device.

[0018] This disclosure contemplates any suitable network 110. As an example and not by way of limitation, one or more portions of network 110 may include an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, or a combination of two or more of these. Network 110 may include one or more networks 110.

[0019] Links 150 may connect client system 130, social-networking system 160, and third-party system 170 to communication network 110 or to each other. This disclosure contemplates any suitable links 150. In particular embodiments, one or more links 150 include one or more wireline (such as for example Digital Subscriber Line (DSL) or Data Over Cable Service Interface Specification (DOCSIS)), wireless (such as for example Wi-Fi or Worldwide Interoperability for Microwave Access (WiMAX)), or optical (such as for example Synchronous Optical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links. In particular embodiments, one or more links 150 each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular technology-based network, a satellite communications technology-based network, another link 150, or a combination of two or more such links 150. Links 150 need not necessarily be the same throughout network environment 100. One or more first links 150 may differ in one or more respects from one or more second links 150.

[0020] FIG. 2 illustrates an example artificial reality system 200. In particular embodiments, the artificial reality system 200 may comprise a headset 204 (e.g., a head-mounted display (HMD)), a controller 206, and a computing system 208. A user 202 may wear the headset 204 that may display visual artificial reality content to the user 202. The headset 204 may include an audio device that may provide audio artificial reality content to the user 202. The headset 204 may include one or more cameras which can capture images and videos of environments. The headset 204 may include an eye tracking system to determine the vergence of the user 202. The headset 204 may include one or more display screens for rendering the artificial reality content. The controller 206 may comprise a trackpad and one or more buttons. The controller 206 may receive input from the user 202 and relay the input to the computing system 208. The controller 206 may also provide haptic feedback to the user 202. The computing system 208 may be connected to the headset 204 and the controller 206 through cables or wireless connections. The computing system 208 may control the headset 204 and the controller 206 to provide the artificial reality content to and receive input from the user 202. The computing system 208 may be a standalone host computer system, an on-board computer system integrated with the headset 204, a mobile device, or any other hardware platform capable of providing artificial reality content to and receiving input from the user 202. In this disclosure, the terms of “headset” and “head-mounted display” may be interchangeably used to refer to a head-mounted device for the artificial reality system.

[0021] A vergence distance may be a distance from the user’s eyes to objects (e.g., real-world objects or virtual objects in a virtual space) that the user’s eyes are converged at. A focus distance may be a distance from the user’s eyes to the objects that the user’s eye are accommodated to. In real world, when the two eyes of a user are gazing at a real object, the two eyes both are converged and accommodated to that object. The vergence distance and focal distance of the two eyes match with each other. In artificial reality, the user may gaze at virtual objects rendered on a head-mounted display. The user’s two eyes may converge to the virtual objects, which can be relatively far from the user in a virtual space, while being accommodated on the head-mounted display, which is relatively close the user’s eyes. The mismatch between the vergence and the accommodation of the user’s eyes may lead to vergence accommodation conflict which may negatively impact the artificial reality experience. For example, vergence accommodation conflict may over time cause eye strain or onset of VR sickness to the user.

[0022] FIG. 3 illustrates an example situation for vergence accommodation conflict in a head-mounted display 300. The head-mounted display 300 may have a display screen 320 for displaying content to a user’s eyes 302 and 304. The display 320 may render a virtual object 322 to the user. The user’ two eyes of 302 and 304 may be gazing at the virtual object 322. In this situation, the vergence distance 342 or gaze depth of the user’s two eyes is corresponding to the virtual distance between the eyes (302, 304) and the virtual object 322. However, the two eyes 302 and 304 may have a focal distance 340 because they are accommodated to the display screen 320 which is the actual light source for this virtual object 322. The mismatch between the focal distance 340 and the vergence distance 342 causes the vergence accommodation conflict which may negatively impact the artificial reality experience provided by the head mounted-display 300. Particular embodiments solve the vergence accommodation conflict problem and improve the user experience for the artificial reality.

[0023] In particular embodiments, the artificial reality headset system may include an eye tracking system for tracking the user’s eyes in real time. The eye tracking system may be a 3D eye tracking system tracking the user’s eye movements (e.g., gazing direction, gazing angle, gazing depth, convergence) and determine where the user is looking at (e.g., vergence distance or gazing point). FIG. 4 illustrates an example 3D eye tracking system 400. The 3D eye tracking system 400 may track three-dimensional eye motion to determine the user’s vergence distance or gazing point. The eye tracking system 400 may include a lens 410, a number of infrared light sources (e.g., 412A-H), a hot mirror 420, and an infrared camera 440. The light sources 412A-H may be infrared LEDs mounted on the lens 410. The hot mirror 420 may be a dichroic filter which reflects infrared light while allowing visible light to pass. The emitted infrared light (e.g., 414) by one or more of the light source 412A-H may reach and be reflected off the eye 450. The reflected light 416 may be further reflected by the hot mirror 420 and reach the infrared camera 440. The camera 440 may be an infrared camera capturing images of the eye 450 using the reflected infrared light. The eye tracking system 400 may capture images of both eyes (e.g., pupils) of the user and process the images using computer vision technology. The eye tracking system 400 may measure the angle of the two eyes and use geometric relations to determine the vergence distance and gazing point of the user. The 3D eye tracking system 400 may measure the user’s eye angle with an accuracy of 1 degree, for example. The visible light 432 from the display screen 430 may pass the hot mirror 420 and the lens 410 to reach the eye 450 allowing the user to see rendered content by the display screen 430.

[0024] In particular embodiments, the headset system may use a machine learning (ML) based approach for eye tracking. The headset system may take a sequence of images of the eyes of the user wearing the headset (e.g., using a 3D eye tracking system) and use the machine learning (ML) algorithm to process the images and output vergence information. For example, the machine learning (ML) algorithm may include an inference model to determine the vergence distance and gazing point of the user. In particular embodiments, the headset system may include a hybrid approach combining 3D eye tracking and ML-based eye tracking.

[0025] However, the tracking system may not work in optimal condition all the time. For example, the eye tracking system may not be able to detect pupils if the headset is worn incorrectly by the user. As another example, the eye tracking system may have reduced accuracy and precision due to malfunctions or user error. As another example, the eye tracking data may be out of range or these is no data from the eye tracking system at all. Furthermore, some artificial reality headset systems may not even include any eye tracking system. Without reliable eye-tracking information, the artificial reality headset system’s ability to ameliorate vergence-accommodation conflicts would be impaired.

[0026] In particular embodiments, the headset system may detect malfunctions of the eye tracking system. Upon detection of malfunctions, the headset system may switch states to receive one or more inputs and use a combination of these inputs to determine the vergence distance or gazing point of the user. These inputs may be based on different approaches including, for example, but not limited to, eye tracking based approaches (e.g., 3D eye tracking, ML based eye tracking), body-based approaches (e.g., head position/movement, hand position/movement, body position/movement), and content-based approaches (e.g., Z-buffer, face/object recognition, developer provided information). Particular embodiments may provide more robust eye tracking using the combination of approaches. A fusion algorithm may weight the inputs based on all these approaches and determine where the user is likely looking at, the Z depth of the display screen, and the confidence score. In particular embodiments, the fusion algorithm may determine correlations between one or more inputs and determine where the user is likely looking at based on the correlations of the inputs. For example, when the headset system detects that the user’s hand has picked up a virtual object and is moving towards his face, the fusion algorithm may infer that the user is looking at the virtual object in his hand. Upon identifying the virtual object as the likely subject of the user’s gaze, the headset system may determine an appropriate Z depth for the display screen. Then, the headset system may physically move the display screen associated with a varifocal system to a position corresponding to the Z-depth to solve the vergence accommodation conflict.

[0027] FIG. 5 illustrates an example head-mounted display 500 having an adjustable display screen 502. The head-mounted display 500 may have a display screen 502 and a lens 504. In particular embodiments, the display screen 502 may be moved along an axis 506 toward the lens 504 or away from the lens 504 within a movable range 520 (e.g., 1 cm) between the positions 512 and 514. The head-mounted display 500 and the lens 504 may have a distance which may be called Z-distance or Z-depth 530. The Z-depth 530 may affect the focus distance of the user’s eyes. The position 512 of the display screen 502 may correspond to a situation in which the user is looking at a virtual object with a vergence distance of 25 cm. The position 514 may correspond to a situation in which the user is looking at a virtual object with a vergence distance of infinite. The lens 504 or other parts of the head-mounted display 500 may be used a reference when adjusting the display screen 502. In particular embodiments, the adjustable display screen may be associated with a varifocal system of the head-mounted display 500. The varifocal system may use the Z-depth 530 of the display screen to reconcile the focus distance and the vergence distance of the user to ameliorate the vergence accommodation conflict. In particular embodiments, the head-mounted display 500 may move an optics block associated with the lens 504 to adjust the Z-depth 530 to ameliorate the vergence accommodation conflict. In particular embodiments, the head-mounted display 500 may move both of the display screen 502 and an optics block associated with the lens 504 to adjust the Z-depth 530 to ameliorate the vergence accommodation conflict. In particular embodiments, the headset may render different images to the user based on the user’s vergence distance or gazing point to eliminate or ameliorate the vergence accommodation conflict.

[0028] In particular embodiments, the headset system may determine one or more performance metrics and compare the performance metrics to one or more performance thresholds to evaluate of the eye tracking system performance and determine the combination of approaches accordingly. FIG. 6 illustrates an example performance evaluation chart with different combinations of the eye tracking based inputs, body-based inputs, and content-based inputs. The horizontal axis 602 may correspond to a performance metric level of the eye tracking system. The vertical axis 604 may correspond to different inputs and/or approaches under different performance conditions. The performance metric may be compared to a first threshold 610 and a second threshold 620. When the performance metric is above the first threshold 610, the eye tracking system may perform as expected in a great condition and the performance may be identified as great performance. In this situation, the headset system may continue to use the eye tracking data from the eye tracking system to determine the vergence distance and gazing point of the user and no other data or inputs are needed.

[0029] When the performance metric is below the first threshold 610 and above the second threshold 620, the performance may be identified as poor. In this situation, the eye tracking system be partially working but have some malfunctions which negatively impact the performance of the eye tracking system (e.g., reducing confidence score, reducing accuracy or/and precision of vergence distance and Z-depth determination). When the eye tracking system has poor performance, the headset system may determine a combination of inputs to determine the vergence distance and gazing point of the user with improved quality and confidence score. The combination may include eye tracking data, body-based inputs, or content-based inputs. For example, the combination may include one or more inputs of the body-based inputs. As another example, the combination may include one or more inputs of the content-based inputs. As another example, the combination may include one or more inputs of both of the body-based inputs, the content-based inputs, and the eye tracking data.

[0030] When the performance metric is below the second threshold hold 620, the eye tracking system may be identified as non-functional. In this situation, the headset system may have no eye tracking data available because the headset system does not have an eye tracking system or the eye tracking system fails to function. When the eye tracking system has poor performance, the headset system may use a combination of inputs to determine the likely vergence distance and gazing point of the user. The combination may include one or more inputs of the body-based inputs or content-based inputs.

[0031] In particular embodiments, the performance metrics may include, for example, but are not limited to, an accuracy of a parameter of the eye tracking system, a precision of a parameter of the eye tracking system, a value of a parameter of the eye tracking system, a detectability of pupil(s), a metric based on one or more parameters associated with the user, a parameter change, a parameter changing trend, a data availability, a weighted combination of one or more performance metrics or related parameters, etc. The thresholds for the performance metric may include, for example, but are not limited to, a pre-determined value, a pre-determined range, a state of a data, a changing speed of a data, a trend of a data change, etc. In particular embodiments, the thresholds may be pre-determined by developers. In particular embodiments, the thresholds may be determined by inputs from a user using the headset or may be determined adaptively using a machine learning or deep learning algorithm using current or historical data of the headset. In particular embodiments, the headset system may detect one or more malfunctions of the eye tracking system using the performance metrics. In particular embodiments, the headset system may detect malfunctions of the eye tracking system by comparing two or more parameters (e.g., information from different sensing channels) of the eye tracking data and determining whether the parameters conform to each other.

[0032] As an example and not by way of limitation, the headset system may compare a parameter value (e.g., Z-depth) of the eye tracking data to a predetermined value or range (e.g., Z-depth range as specified in a specification or manual of the headset) and determine whether the parameter value is within the predetermined range. When the parameter value is out of range, the eye tracking system may be identified as malfunctioning. As another example, the headset system may determine a changing trend of a parameter of the eye tracking data and determine that the parameter value is drifting and the deviation is beyond an acceptable range. As another example, the headset system cannot receive data from the eye tracking system and may determine that the headset does not include an eye tracking system or the eye tracking system fails to function. As another example, the eye tracking system may be not able to detect the user’s pupils when the user blinks or occluded by other means. As another example, the headset system may detect that the user’s eyes have some problems (e.g., eye rheology or two eyes don’t converge) which prevent the eye tracking system from working appropriately.

[0033] In particular embodiments, the headset system may determine one or more parameters related to the user wearing the headset and determine that the eye tracking system performance may be negatively impacted. The user related parameter may include, for example, but are not limited to, a distance between two eyes (e.g., pupils) of the user, a pupil position, a pupil status, a correlation of two pupils of the user, a head size of the user, a position of a headset worn by the user, an angle of the headset worn by the user, a direction of the headset worn by the user, an alignment of the eyes of the user, an alignment of headset with the user’s eyes, a weighted combination of one or more related parameters associated with the user, etc. The headset may compare the user related parameters to one or more standards which may be pre-determined by developers or may be adaptively determined by users or algorithms. When the user related parameters fail to meet the standards, the headset system may determine that the eye tracking system cannot perform well or cannot function in these situations. As another example and not by way of limitation, the headset system may detect that the user is wearing the headset incorrectly (e.g., incorrect direction, posture, or alignment) and the eye tracking data is not available or not accurate. As another example, the headset system may not be able to detect the user pupils and determines that the eye tracking system cannot track the eyes of the current user. As another example, the headset system may not be able detect or track the user’s eyes correctly because the user is wearing prescription lenses or contact lenses which are beyond the range of support for the headset system. As another example, the headset system may determine that the user has larger pupil distance or larger head size than the headset system is designed for. In this situation, the eye tracking system may not be able to detect the pupils or may not be able to track the user gaze correctly.

[0034] In particular embodiments, the headset system may determine a confidence score for the determined vergence distance or gazing point of the user and the Z-depth of the display screen. The headset system may compare the confidence score to a confidence level threshold to determine whether the determined vergence distance or gazing point meets the pre-determined requirements (e.g., precision, accuracy, updating rate, stability). In particular embodiments, the headset system may constantly evaluate quality of the determined vergence distance or gazing point using the confidence score to determine whether further data is needed to improve the determination quality. For example, the headset system may determine the vergence distance and gazing point based on the body-based inputs and the confidence score being above the confidence level threshold. In this situation, there is no need for other data other than the body-based inputs. As another example, the headset system may determine that the confidence score for the determined vergence distance or gazing point does not meet the pre-determined requirements and the headset system needs further data (e.g., more body-based input, eye tracking data, or content-based inputs) to improve the determination quality and confidence score.

[0035] When the performance metric is below the first threshold, the eye tracking system performance may be poor or non-functional. The headset system may receive one or more first input associated with the body of a user wearing the headset. The headset system may determine a region that the user is looking at within the field of view of a head-mounted display of the headset worn by the user. The region that the user is looking at may be determined based on the received one or more first inputs associated with the body of the user. The headset system may compare the region that the user is looking at with locations of one or more objects in a scene displayed on the head-mounted display to determine which objects in the scene fall in that region. Then, the headset system may determine the likely vergence distance or gazing point of the user based on the one or more first inputs associated with the body of the user, the region that the user is looking at, and/or the displayed objects in the scene that fall in that region. The vergence distance may be a distance from the user’s eyes to the virtual objects, assuming the user is looking at the virtual objects. A gazing point may be a point in the virtual space where the user is gazing at. The headset system may adjust the position of a display screen of the head-mounted display based on the determined vergence distance of the user. In particular embodiments, the headset system may determine the vergence distance or gazing point based on one or more content-based inputs other than the body-based inputs. In particular embodiments, the headset system may determine the vergence distance or gazing point based on both the body-based inputs and content-based inputs. In particular embodiments, the headset system may determine the vergence distance or gazing point based on the eye tracking data, the body-based inputs, and the content-based inputs. In particular embodiments, the headset system may adjust one or more configurations of the head-mounted display based on the determined vergence distance or gazing point of the user to eliminate or ameliorate the vergence accommodation conflict. The headset system may configure the head-mounted display by rendering different images to the user, adjusting a position of a display screen, or adjusting an optics block based on the determined vergence distance or gazing point of the user.