Meta Quest Passthrough Camera Access (PCA) for Unity — access the forward-facing RGB cameras on Quest 3 / Quest 3S to feed Computer Vision and Machine Learning pipelines. Use when capturing the passthrough camera image/texture, reading the camera pose and intrinsics, projecting camera pixels into world space via `PassthroughCameraAccess.ViewportPointToRay`, wiring camera frames into ML/CV models, or reasoning about resolution, permissions, vendor tags, and the pinhole/principal-point model. For projecting the image onto a flat world-space surface (frustum-slice quad / image-plane overlay) see references/principal-point-offset.md; for placing 2D ML detections as world-space 3D bounding boxes see references/detection-bounding-boxes.md. Skip if the user only wants to cast the user's POV (use the Media Projection API instead) or is doing screen-space-only overlays.
npx skills add https://github.com/meta-quest/agentic-tools --skill hz-unity-passthrough-camera-access
Building on top of the Android Camera2 API,
Passthrough Camera Access provides access to the forward-facing RGB cameras on
Quest 3 and Quest 3S for the purpose of supporting Computer Vision and Machine
Learning. This API is distinct from the
which supports casting from the user's POV (including the UI) and should be used
if your purpose is to represent what the user is seeing. Use the Passthrough
Camera API to add application-specific computer-vision capabilities that extend
the understanding of the user's environment and actions beyond what is provided
by the Quest Scene API.
This API provides an unobstructed view using the forward-facing RGB cameras and
can be integrated with ML/CV pipelines. Common use cases:
application can interact with (e.g. fitness equipment like dumbbells,
industrial equipment like audio mixers).
where looking at a painting surfaces information about its history, artist,
and style using off-device LLMs.
user to perform a task, the app can detect whether it was done correctly by
interpreting changes to the environment (e.g. writing on a whiteboard).
to modify a texture, much more realistic design effects can be achieved.
android.permission.CAMERA orhorizonos.permission.HEADSET_CAMERA. CAMERA grants access to both the
passthrough and avatar cameras; HEADSET_CAMERA grants access only to the
passthrough camera.
Camera API.
Camera Access on Quest is implemented on top of
within Horizon OS. Starting from Horizon OS v74, Camera2 and its Unity
extension are available on Quest headsets (Horizon OS v83 added an Unreal
extension). On Quest 3 and Quest 3S developers have access to the left and right
cameras on the face of the HMD.
The Android Camera2 API provides:
configuration information.
a new OS, the RGB channels might flip causing color distortion (e.g. flesh
tones appear bluish). Reboot the device to fix.
smaller than what the user sees. The 1280×1280 resolution added in v83
expands the vertical field of view, but still does not cover the entire
passthrough view.
accessing the Passthrough Camera API) are not applied if the application is
installed through the Meta Quest Developer Hub.
is covered by the Developer Data Use Policy. Adhere to this policy fully.
can enable compelling use cases, but keep experiences comfortable by
maintaining a high framerate.
Detailed, task-specific guidance for projecting and placing PCA imagery in world
space lives under references/:
Correctly project the camera image onto a flat world-space surface
(frustum-slice quad, FOV visualizer, image-plane overlay) using the optical
axis as the plane normal. Read this when corner rays from ViewportPointToRay
produce a non-rectangular quad, a camera-aligned quad drifts off-center, or
2 * d * tan(fov / 2) gives the wrong size on Phoenix/Stanley but worked on
Quest 3.
Draw precise 3D bounding boxes around real-world objects detected by an ML
model. Read this when placing 2D ML detections (YOLO, SSD, DETR, custom CNN)
into world space, building a camera-facing billboard quad from a 2D detection,
or wiring PassthroughCameraAccess + EnvironmentRaycastManager for object
localization.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take meta-quest/hz-unity-passthrough-camera-access from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.