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Omniverse Realtime Viewer

NVIDIAOfficialCreated by NVIDIA Omniverse

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk
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From the author

Use as the top-level router for Omniverse Realtime Viewer USD apps, XR delivery, SPG GPU processing, stylized rendering, and focused viewer reference documents.
— the description by NVIDIA Omniverse

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Omniverse Realtime Viewer skill acts as a central routing and architectural framework for developing USD-based 3D viewer applications. It provides a structured approach to composing rendering, scene state, and delivery mechanisms, ensuring that developers can integrate OVRTX rendering, OVStage scene management, and various UI or streaming adapters effectively. The skill emphasizes a modular architecture where the viewer runtime, UI/input adapters, and delivery systems are decoupled, allowing for consistent behavior across local and streamed environments.

What it does

  • Provides a routing architecture for USD viewer apps, XR delivery, and GPU-accelerated rendering.
  • Defines the separation of concerns between viewer runtime, UI adapters, and delivery systems.
  • Offers guidance on integrating OVRTX for high-fidelity 3D rendering.
  • Establishes conventions for camera interaction, scene state management, and input handling.
  • Supports the implementation of specialized features like stylized rendering and CAE/CFD visualization.
  • Provides protocols for agent-to-UI communication and scene control.

When to use it

  • When starting the development of a new USD-based realtime viewer application.
  • When integrating advanced rendering features like stylized visualization or AOV switching.
  • When building XR or spatial editing capabilities into an existing Omniverse application.
  • When you need to standardize the architecture for local and streamed viewer deployments.
  • When implementing chat-driven scene control or agent-based inspection of a 3D scene.

Advantages

  • Promotes a clean, modular architecture that separates rendering logic from UI and transport.
  • Ensures consistent behavior across different delivery methods, such as local windows or WebRTC streaming.
  • Provides clear paths for extending functionality without disrupting the core viewer runtime.
  • Facilitates the integration of complex simulation and visualization data into the viewer.
  • Standardizes conventions for scene state, selection, and camera control.

Usage examples

“How do I set up a basic USD viewer application using the Omniverse Realtime Viewer architecture?”
The agent will guide you to start with the viewer app reference, resolve dependencies, and implement the core runtime owner to manage the OVStage and OVRTX renderer.
“I need to add stylized rendering to my existing viewer. Where should I start?”
The agent will direct you to the stylized visualization references to implement the required USD/Lua/CUDA nodes within your existing runtime owner.
“How can I enable an AI agent to control the viewer scene?”
The agent will point to the A2UI Scene Copilot reference to implement an operation interface that allows the agent to send commands through the existing runtime owner.
“How do I switch between different render outputs like beauty and depth in my viewer?”
The agent will explain how to use the AOV switching reference to map RenderVars and update the RenderProduct's orderedVars through the runtime owner.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/omniverse-realtime-viewer/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip omniverse-realtime-viewer.zip -d .agents/skills

Documentation by NVIDIA Omniverse

<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->

<!-- SPDX-License-Identifier: Apache-2.0 -->

Omniverse Realtime Viewer

Build responsive USD viewers by composing OVRTX rendering, OVStage scene state, and the delivery/UI libraries appropriate to the app. Use this package for viewer architecture and interaction; use each selected SDK's upstream guidance for installation and exact release-specific APIs.

Start With The App

  1. When delivery is explicit, select its recipe through references/routing.md and follow its current phase. Otherwise start with references/usd-viewer-app/README.md. For an existing app, keep its architecture and read only the references for the requested change. Broad app, conventions and UI catalogs are lookups as needed.

  2. Resolve the selected libraries through references/dependencies/README.md. Preserve the project's manifest unless an upgrade is requested.

  3. Build a working frame and camera interaction in the chosen shell, then add the requested capabilities. Read references/conventions.md for shared input, camera, selection, and protocol conventions.

  4. Use references/viewer-ux-workflow/README.md for broader interface design. Loading, selection feedback, errors, and input responsiveness belong in the first working app, alongside rendering.

  5. Validate the actual delivery path with references/validation.md. A browser screenshot does not validate a native window or a different transport.

Architecture

OwnerResponsibility
Viewer runtimeOwn the live OVStage, publication clock, OVRTX renderer, scene changes, and render stepping.
UI and input adapterDisplay frames and copied app state; send user intent to the runtime owner.
Storage/query/simulation workersPerform their assigned work and return results through the selected integration boundary.
Delivery adapterPresent locally or stream through ovstream; respect frame ownership and backpressure.

Use OVRTX for USD/3D rendering. Browser apps display its WebRTC video; local apps present its output through their native shell. A missing GPU means rendering is unverified, not a reason to substitute a browser 3D renderer.

Keep viewer cameras, settings, effects, and transient edits in app-owned session or runtime state so the source USD stays intact. Serialize scene mutation and render stepping through the runtime owner. Read references/ovstage-ovrtx-integration/README.md for that loop, then the population or data-plane references when implementing scene loading or attribute writes.

Keep expensive loading and metadata work off the UI and render threads. Keep transport details behind a delivery adapter so camera, selection, properties, and loading state can be reused across local and streamed shells.

Add Capabilities As Needed

Use references/routing.md to select camera/picking, scene management, render settings, cloud assets, simulation, editor tools, deployment, or CAE/CFD visualization. Read the selected capability's implementation flow and its linked contract; the full reference catalog is not a prerequisite for a first frame.

For simulation, use the routing reference's Simulation Ecosystem section plus references/realtime-sim-integration/README.md. For UI-driven forces or control, add references/interaction-round-trip/README.md.

For example scenes, use references/usd-sample-data/README.md. When a runtime failure needs diagnosis, use references/troubleshooting/README.md and the selected SDK's matching release guidance before changing the architecture.

Solid Modeling: Upstream First

For usd-brep, exact BRep, solid modeling, kernel failures, CAD topology, or BrepArray questions, first read references/dependencies/README.md → USD BRep. Follow the owning repository's current agent guidance, skills, operation docs, and package API before selecting local integration references. Discover the upstream router rather than assuming skill names or directory layouts remain fixed. Upstream owns modeling semantics, schema rules, tuning, and diagnostics; this package owns OVRTX display, worker handoff, selection mapping, and publication timing.

GPU Processing And Stylized Viewers

For custom GPU processing, chained outputs and lens sampling, read references/spg-processing/README.md. For toon, ink, technical/X-Ray styles, geometry strokes or full wireframe, continue to references/stylized-visualization/implementation.md. Its USD/Lua/CUDA starting node and geometry/depth/composite build sequence work with the selected viewer recipe; use references/stylized-visualization/style-recipes.md for the preset math and references/stylized-visualization/README.md for supporting contracts. Implement the result inside the existing OVRTX/runtime owner and deliver the published processed output. These instructions do not require a sample checkout or a prior stylized-rendering implementation.

For an optional usd-brep geometry source, first read references/dependencies/README.md → USD BRep and the owning repository's current agent guidance, skills, and operation docs; then read references/stylized-visualization/usd-brep-source.md for the display boundary.

XR Viewers

For headsets, immersive viewers, or spatial editors, start with references/xr-viewer-recipe/README.md. Resolve SDK setup and contracts through references/dependencies/ovxr.md; the library owns OpenXR lifecycle and frame submission. For controllers, hands, navigation, or scene tools, add references/xr-spatial-input/README.md and references/ovxr-python-interactions/README.md. For projection-integrated UI, read references/ovxr-composition-ui/README.md and references/ovxr-composition-ui-validation/README.md.

Files

references/camera-auto-select/README.md

Camera Auto-Select

Purpose

When a user says "here's my stage" the viewer should open to a meaningful camera on the very first frame — not to an arbitrary default position. This skill inspects the stage for authored cameras and picks the best one, or computes a fit-all fallback.

Triggers

Use when:

  • Loading a user-provided USD stage for the first time.

  • Building a viewer application that must "just work" with arbitrary stages.

  • The default camera position is wrong or unhelpful for a given scene.

  • A build pipeline needs to determine the hero camera before deployment.

Priority Heuristic

Evaluate cameras in this order. Stop at the first match.

PriorityConditionRationale
1Stage metadata has defaultCameraAuthor explicitly chose one.
2Camera prim named *Main*, *Hero*, *Default*, *Persp* (case-insensitive)Common naming convention.
3Exactly one camera in the stageNo ambiguity — use it.
4Camera with widest FOV (lowest focalLength) that is NOT top-down (X-rotation ≈ 90°)Likely the overview/hero shot.
5First camera in scene traversal orderDeterministic fallback.
6Compute bbox-fit cameraStage has no authored cameras at all.

Runtime Boundary

For an attached OVStage viewer, obtain camera metadata and bounds through the application runtime adapter. The adapter owns the active stage, returns copied camera DTOs, and applies the selected initial camera through the OVStage data plane before publishing its ordinal. Do not open a second Usd.Stage, retain a USD prim outside that boundary, or use renderer attribute APIs as an alternate source of camera state.

The pxr helpers below are for an explicitly separate offline/preflight path: for example, inspecting a known USD file before an application creates its runtime. They are not part of an attached viewer live load, query, or write path.

Offline / Preflight Implementation (Python / pxr)

import math
from pxr import Usd, UsdGeom, Gf

HERO_NAME_PATTERNS = ["main", "hero", "default", "persp", "perspective"]


def find_best_camera(stage: Usd.Stage) -> str | None:
    """Return the prim path of the best camera, or None if bbox-fit needed."""

    # Priority 1: explicit defaultCamera in layer metadata
    root_layer = stage.GetRootLayer()
    default_cam = root_layer.customLayerData.get("defaultCamera")
    if default_cam:
        prim = stage.GetPrimAtPath(default_cam)
        if prim and prim.IsA(UsdGeom.Camera):
            return str(prim.GetPath())

    # Collect all cameras
    cameras = []
    for prim in stage.Traverse():
        if prim.IsA(UsdGeom.Camera):
            cameras.append(prim)

    if not cameras:
        return None  # caller should use bbox-fit

    # Priority 2: name matching
    for cam in cameras:
        name_lower = cam.GetName().lower()
        for pattern in HERO_NAME_PATTERNS:
            if pattern in name_lower:
                return str(cam.GetPath())

    # Priority 3: single camera
    if len(cameras) == 1:
        return str(cameras[0].GetPath())

    # Priority 4: widest FOV, skip top-down
    best_cam = None
    lowest_focal = float("inf")
    for cam in cameras:
        focal = cam.GetAttribute("focalLength").Get() or 50.0
        if _is_top_down(stage, cam):
            continue
        if focal < lowest_focal:
            lowest_focal = focal
            best_cam = cam

    if best_cam:
        return str(best_cam.GetPath())

    # Priority 5: first in traversal
    return str(cameras[0].GetPath())


def _is_top_down(stage: Usd.Stage, cam_prim) -> bool:
    """Heuristic: camera looking straight down (X rotation ~90°)."""
    xformable = UsdGeom.Xformable(cam_prim)
    xform = xformable.ComputeLocalToWorldTransform(Usd.TimeCode.Default())
    # Extract the forward vector (negative Z in camera space)
    forward = xform.TransformDir(Gf.Vec3d(0, 0, -1))
    up_axis = UsdGeom.GetStageUpAxis(stage)
    if up_axis == UsdGeom.Tokens.z:
        world_down = Gf.Vec3d(0, 0, -1)
    else:
        world_down = Gf.Vec3d(0, -1, 0)
    # If forward is within ~10° of straight down, it's top-down
    dot = Gf.Dot(forward.GetNormalized(), world_down)
    return dot > math.cos(math.radians(10))
Bbox-Fit Fallback

When no authored camera exists, compute a fit-all orbit. Pass the smaller of the horizontal and vertical field of view so portrait viewports also fit:

def compute_bbox_fit_camera(stage: Usd.Stage, fov_deg: float = 60.0):
    """Return (target, distance, elevation, azimuth) for an OrbitCamera."""
    bbox_cache = UsdGeom.BBoxCache(Usd.TimeCode.Default(), ["default", "render"])
    world_bbox = bbox_cache.ComputeWorldBound(stage.GetPseudoRoot())
    bbox_range = world_bbox.ComputeAlignedBox()

    center = (bbox_range.GetMin() + bbox_range.GetMax()) / 2.0
    size = bbox_range.GetMax() - bbox_range.GetMin()
    radius = math.sqrt(sum(float(component) ** 2 for component in size)) / 2.0

    # Distance to fit the bounding sphere in view
    half_fov = math.radians(fov_deg / 2.0)
    distance = max(radius, 1e-3) / math.sin(half_fov) * 1.2  # 20% padding

    # Default orbit angles: slight elevation, 3/4 azimuth
    elevation = math.radians(25.0)
    azimuth = math.radians(-45.0)

    return {
        "target": [center[0], center[1], center[2]],
        "distance": distance,
        "elevation": elevation,
        "azimuth": azimuth,
    }
Emitting camera_config.json

During offline preflight, write a config the frontend can consume:

import json

def emit_camera_config(stage: Usd.Stage, output_path: str = "camera_config.json"):
    """Write camera config for the frontend."""
    cameras = []
    for prim in stage.Traverse():
        if prim.IsA(UsdGeom.Camera):
            cam = UsdGeom.Camera(prim)
            cameras.append({
                "path": str(prim.GetPath()),
                "name": prim.GetName(),
                "focalLength": cam.GetFocalLengthAttr().Get() or 50.0,
            })

    best = find_best_camera(stage)
    config = {
        "cameras": cameras,
        "defaultCamera": best,
        "hasBboxFallback": best is None,
    }

    if best is None:
        config["bboxFit"] = compute_bbox_fit_camera(stage)

    with open(output_path, "w") as f:
        json.dump(config, f, indent=2)

    return config

Integration Points

Attached OVStage Viewer (stage-loading)

After the runtime owner has populated the composed scene, ask its camera query adapter for copied camera metadata, default-camera metadata, and a bounds summary. Apply the selected authored camera or bbox-fit pose through the camera command adapter. That command owns the OVStage transform/lens writes, waits for them, advances the write floor, and returns the committed ordinal for the next render.

camera_state = runtime.camera_queries.initial_view()

if camera_state.authored_camera_path:
    committed = runtime.camera_commands.use_authored_camera(
        camera_state.authored_camera_path
    )
else:
    committed = runtime.camera_commands.apply_bbox_fit(camera_state.bbox_fit)

renderer.step(render_products, delta_time, ordinal=committed.ordinal)

The method names are an application adapter contract, not OVStage API names. Implement them from the current OVStage population/query/data-plane examples and the current OVRTX attached-stage guidance. Keep all USD and OVStage handles inside the runtime owner.

Offline / Preflight Tool

An offline tool may call Usd.Stage.Open() and the helpers above to emit a static camera_config.json. Treat that output as a hint: the attached viewer must re-query its active OVStage generation after population or scene switching.

Frontend (streaming-client)

On stage load response, read the camera list and set the initial view. If the app includes a camera picker (see camera-picker skill), populate it from the same data.

Build Pipeline

For pre-built/deployed apps where the stage is known at build time, run emit_camera_config() during the build step and bundle the JSON with the app assets. The frontend reads it at startup without needing a round-trip to the server.

Gotchas

  • defaultCamera in layer metadata is a custom field — not all stages set it. The heuristic handles this gracefully.

  • Some stages define cameras inside referenced assets (props with internal cameras). Filter to cameras under /World/Cameras or at the root level to avoid picking internal asset cameras.

  • Top-down cameras are useful for plan views but make poor defaults for first impressions. The heuristic deprioritizes them.

  • For multi-GPU or multi-viewport setups, each viewport can have its own camera. This skill picks the initial default only.

  • In an attached viewer, invalidate camera DTOs on a stage-generation change; never reuse an offline pxr prim or a previous runtime path handle.

See Also

  • camera-controls — orbit, pan, zoom, and fly input handling.

  • camera-picker — UI dropdown for switching between stage cameras.

  • stage-loading — stage open and session setup.

  • stage-hierarchy — traversal and bbox computation.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python, Needs Node.js

The author says: Orchestrator skill. Downstream focused references may require NVIDIA GPUs, ovrtx, ovstream, ovui, OpenUSD, Python, Node/React, Tauri, Electron, C++, or cloud GPU deployment access depending on the selected viewer path.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Omniverse
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/omniverse-realtime-viewer · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
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