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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://mp-staging.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/huggingface-usd/README.md

Hugging Face USD Asset Acquisition

Prerequisites

Install the Hugging Face SDK (provides both Python API and hf CLI):

pip install -U "huggingface_hub[cli]"

Authentication

ScenarioAuth needed?
Public datasets (e.g., NVIDIA SimReady)No
Gated datasetsAccept terms on HF website + token
Private datasetsAccess granted + token
Higher rate limitsToken recommended

Setup:

# Interactive (stores at ~/.cache/huggingface/token)
hf auth login

# Or via environment
export HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxx"

# Or inline
hf download <dataset> --token hf_xxx ...

Create tokens at: https://huggingface.co/settings/tokens — Read scope is sufficient.

Downloading Assets

Single file
hf download <org>/<dataset> <path/to/file.usd> --repo-type dataset --local-dir assets
Entire asset folder (includes dependencies like textures/sublayers)
hf download <org>/<dataset> --repo-type dataset --local-dir assets --include "<folder>/*"
Multiple patterns (batch)
hf download <org>/<dataset> --repo-type dataset --local-dir assets \
  --include "Props/assembly/Pallets_*/*" \
  --include "Props/general/Warehouse/Forklift*/*"
Full dataset
hf download <org>/<dataset> --repo-type dataset --local-dir assets

Always use --repo-type dataset — HF defaults to model repos otherwise.

Hero Stage Discovery

When a user provides a Hugging Face dataset URL and wants "the USD to open", use these heuristics to identify the correct entry-point file (the "hero stage"):

Decision process

  1. List root-level files in the repository (not in subdirectories)

  2. Filter for USD files (.usd, .usda, .usdc)

  3. If exactly one root-level USD exists — that's almost certainly the hero

  4. If multiple exist, score them using the signals below

Scoring signals (strongest first)
SignalConfidenceHow to check
Has a .thumbs/256x256/<filename>.png thumbnailVery highCheck if .thumbs/ folder contains a matching PNG
Has a defaultPrim setVery highOpen with pxr → stage.GetDefaultPrim() is valid
References sublayersHighstage.GetRootLayer().subLayerPaths is non-empty
Name matches dataset nameHighe.g., physical_ai_simready_warehouse_01.usd in dataset PhysicalAI-SimReady-Warehouse-01
Larger file size than siblingsMediumScenes are bigger than individual props
Name contains "scene", "stage", "world", "main"MediumCommon naming conventions
A SubLayers/ folder exists in the repoSupportingConfirms the root USD is a composed scene
What is NOT the hero

  • Files inside Props/ — these are individual assets/components

  • Files inside SubLayers/ — these are scene partitions referenced by the hero

  • Files inside Materials/ or Textures/ — supporting data

  • Files with _physics suffix — physics layer overrides, not visual entry points

Without pxr available

If you can't inspect USD structure programmatically, rely on:

  1. Position (root-level, not in a subdirectory)

  2. Naming (matches dataset name)

  3. Thumbnail presence (.thumbs/256x256/)

  4. Context (does a SubLayers/ folder exist?)

These four signals together are sufficient to identify the hero in all known NVIDIA SimReady datasets.

Dependency Resolution

Once you identify the hero stage, determine what else to download:

Hero characteristicsWhat to download
No sublayers, no external referencesJust the hero file — self-contained
Has sublayers onlyHero + SubLayers/*
Has sublayers + payload references to Props/Full dataset for complete rendering
Unknown/complexDownload everything to be safe

For a quick structural preview (walls/floor render, some props missing):

hf download <org>/<dataset> --repo-type dataset --local-dir assets \
  --include "<hero>.usd" --include "SubLayers/*" \
  --include "Props/assembly/*" --include "Props/modular/*"

For full fidelity (all props, textures, materials resolve):

hf download <org>/<dataset> --repo-type dataset --local-dir assets

Catalog Discovery

Many NVIDIA datasets include a CSV catalog at the repo root. Download it to browse available assets:

hf download <org>/<dataset> <catalog>.csv --repo-type dataset --local-dir .

For nvidia/PhysicalAI-SimReady-Warehouse-01, the catalog is physical_ai_simready_warehouse_01.csv with columns:

  • asset_name — human-friendly name

  • relative_path — path within the repo (use for download commands)

  • classification — category (e.g., "Prop general hand manipulation")

  • label — object type (e.g., "bottle", "Forklift", "Cardboard Box")

Parse it, filter by label or classification, then feed relative_path values into hf download.

End-to-End Example

Developer: "I want to use the warehouse stage at huggingface.co/datasets/nvidia/PhysicalAI-SimReady-Warehouse-01 as my test stage"

  1. Parse URL → dataset is nvidia/PhysicalAI-SimReady-Warehouse-01

  2. List root files → one USD: physical_ai_simready_warehouse_01.usd (27 KB)

  3. Check signals: ✓ root-level, ✓ name matches dataset, ✓ has thumbnail, ✓ SubLayers/ exists

  4. Conclusion: that's the hero stage

  5. Inspect (if pxr available): defaultPrim=/World, upAxis=Z, metersPerUnit=1.0, 6 sublayers

  6. Download for development:

# Structural preview (~24 MB)
hf download nvidia/PhysicalAI-SimReady-Warehouse-01 --repo-type dataset --local-dir ./test-assets \
  --include "physical_ai_simready_warehouse_01.usd" \
  --include "SubLayers/*" \
  --include "Props/assembly/*" \
  --include "Props/modular/*"

# Open: ./test-assets/physical_ai_simready_warehouse_01.usd
# Default prim: /World | Up axis: Z | Meters per unit: 1.0

Curl Fallback (No Python Needed)

For environments where installing packages isn't practical:

# Public dataset file
curl -L --fail -o asset.usd \
  "https://huggingface.co/datasets/<org>/<dataset>/resolve/main/<path>?download=true"

# Authenticated (gated/private)
curl -L --fail -H "Authorization: Bearer $HF_TOKEN" \
  -o asset.usd \
  "https://huggingface.co/datasets/<org>/<dataset>/resolve/main/<path>?download=true"

Verify you got real data: head -c 8 asset.usd should show PXR-USDC (binary) or #usda 1.0 (ASCII), not a git-lfs pointer.

Important Notes

  • huggingface_hub handles Xet-backed repos transparently (many newer HF repos use Xet storage)

  • Downloads cache in ~/.cache/huggingface/ — use --local-dir to place files where you need them

  • Large downloads resume automatically on retry

  • A single .usd may reference textures/sublayers via relative paths — preserve directory structure

  • Individual SimReady props are 1–100 KB; full datasets can be 10+ GB — be selective with --include

Viewer Integration

After downloading the hero stage:

  1. Ensure directory structure is preserved (hero USD uses relative paths like ./SubLayers/...)

  2. Open the hero .usd file — it will resolve references to sublayers and props

  3. Key metadata for viewer configuration:

    • defaultPrim — root prim to load (usually /World)

    • upAxis — coordinate system (Z-up for SimReady)

    • metersPerUnit — scale factor (1.0 = real-world meters)

Known NVIDIA SimReady Datasets on HF

  • nvidia/PhysicalAI-SimReady-Warehouse-01 — 753 warehouse/manipulation props (~14.4 GB total)

    • Hero: physical_ai_simready_warehouse_01.usd

    • 6 sublayers (loading zone, sorting area, unloading/staging, metro racks, floorplan, transporter area)

    • 85 asset labels (Cardboard Box ×151, Warehouse ×43, Pallet ×28, Forklift ×5, etc.)

See also: usd-sample-data, stage-loading, stage-management, cloud-assets.

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

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Download ZIP

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