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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/viewport-overlays/cuda-compositing.md

CUDA Overlay Compositing

Use with viewport overlays. This is app code to implement, not an import from an unshipped overlay package. Resolve ovui, OVRTX, and Warp through dependencies; verify the selected release's headless export wrapper and native implementation before wiring its frame adapter.

Pixel And Ownership Contract

  • Base: a fully initialized, app-owned CUDA uint8 [height, width, 4] BGRA display buffer. Copy/convert OVRTX output using streaming server or your local presentation adapter. Do not write into a mapped OVRTX render var or an encoder-owned frame.

  • Overlay: a separate app-owned CUDA uint8 [height, width, 4] RGBA export buffer on the same GPU/context. Allocate once with wp.empty((height, width, 4), dtype=wp.uint8, device=device). Pass int(buffer.ptr) and the actual byte row pitch int(buffer.strides[0]) to the exporter. Pitch must be at least width * 4, and the allocation must cover every copied row. Check extent/format before each copy after any resize; never resize inside a blend call after copying.

  • Both buffers must use the same image origin and compatible color encoding. Verify asymmetric corner markers to detect a vertical flip. Explicitly convert if the exporter and display buffer use different orientations.

  • Export format rgba8 specifies channel order and storage, not alpha convention. For straight alpha, out = src * a + dst * (1-a); for premultiplied alpha, out = src + dst * (1-a). Choose the mode from a pixel probe of the actual export. ImGui Vulkan drawing over transparent black can produce premultiplied RGB; do not multiply alpha twice.

  • Verify background pixels are (0, 0, 0, 0). An opaque framebuffer clear cannot be fixed by treating dark colors as transparent. Resolve the export/clear behavior with the selected ovui package before calling the overlay complete.

  • The example blends display-encoded bytes over an opaque base and sets output alpha to 255. For color-critical compositing, convert both inputs to a common linear color space, blend there, then apply the display encoding. A transparent base needs the full output-alpha equation instead.

Blend Kernel

This kernel supports straight or premultiplied RGBA over an opaque BGRA base. The caller validates equal extents, device, orientation, and the alpha probe.

import warp as wp

@wp.kernel
def blend_overlay(
    base_bgra: wp.array3d(dtype=wp.uint8),
    overlay_rgba: wp.array3d(dtype=wp.uint8),
    premultiplied: int,
):
    y, x = wp.tid()
    a = wp.int32(overlay_rgba[y, x, 3])
    if a == 0:
        return
    inv_a = wp.int32(255) - a
    source_scale = a
    if premultiplied != 0:
        source_scale = wp.int32(255)
    for c in range(3):
        # RGBA source -> BGRA destination.
        src = wp.int32(overlay_rgba[y, x, 2 - c])
        dst = wp.int32(base_bgra[y, x, c])
        value = (src * source_scale + dst * inv_a + wp.int32(127)) // wp.int32(255)
        base_bgra[y, x, c] = wp.uint8(wp.min(value, wp.int32(255)))
    base_bgra[y, x, 3] = wp.uint8(255)

For a byte-space probe, a straight half-alpha red pixel is approximately (255, 0, 0, 128); the equivalent premultiplied pixel is approximately (128, 0, 0, 128). Both should produce BGRA (0, 0, 128, 255) over black, allowing rounding. Also test opaque red/blue and untouched transparent pixels.

Export, Copy, Release, Blend

Implement the overlay tick adapter using the selected ovui release. The following function assumes the owner has already ticked the overlay and that headless_frame has been initialized. The explicit device synchronizations provide a conservative correctness baseline; measure before replacing them with release-supported stream/event dependencies.

def composite_export(headless_frame, overlay_rgba, base_bgra, *, premultiplied):
    # All buffers are owned by the app and validated before entering this function.
    device = base_bgra.device
    if overlay_rgba.device != device or overlay_rgba.shape != base_bgra.shape:
        raise ValueError("Overlay and base must have matching devices and extents")
    height, width, channels = base_bgra.shape
    if channels != 4 or headless_frame.extent() != (width, height):
        raise ValueError("Export extent must match the four-channel display buffer")
    if headless_frame.format() != "rgba8":
        raise ValueError("Expected RGBA8 overlay export")

    # Some releases enqueue a stream-0 GPU wait; this is not a CPU timeout.
    if not headless_frame.wait_ready(10_000_000):
        return False
    try:
        copied = headless_frame.copy_to_linear(
            int(overlay_rgba.ptr), int(overlay_rgba.strides[0]),
            cuda_stream_handle=0,
        )
        if not copied:
            return False
    finally:
        # Finish the stream-0 wait/copy before releasing the Vulkan image,
        # including failure cleanup. A fatal CUDA error requires owner recovery.
        wp.synchronize_device(device)
        headless_frame.signal_consumed()
        wp.synchronize_device(device)

    wp.launch(
        blend_overlay, dim=(height, width),
        inputs=[base_bgra, overlay_rgba, int(premultiplied)], device=device,
    )
    wp.synchronize_device(device)
    return True

Check CUDA device/context compatibility before initializing interop. Native wait_ready and signal_consumed implementations can enqueue semaphore work on stream 0 even when a copy API accepts a stream argument. A nonzero copy stream therefore does not by itself establish producer or release ordering. Inspect upstream's native implementation before optimizing.

The numeric timeout argument is not proof of bounded execution: in some releases it is not enforced by the native GPU wait. Treat producer failure as an owner/session failure and use process supervision if a hard deadline is required. Do not race teardown against an outstanding GPU semaphore wait.

On failed acquisition/copy, skip blending and preserve the fresh OVRTX base; never blend uninitialized or previous-frame export memory. After acquiring an export, release it exactly once in cleanup. Report persistent errors to the UI. Retain both CUDA allocations and the submitted base frame until GPU work and its downstream consumer have completed. For ovstream, use frame lifetime guidance; for local presentation, follow the selected image/texture adapter's completion contract. Synchronizing the blend only proves the producer has finished, not that an encoder or presenter has released the frame.

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)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA Omniverse or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download omniverse-realtime-viewer.zip