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Please install the Agent Skill "nvidia/physical-ai-neural-reconstruction" for me from skillmd.com. A SkillMD skill is a SKILL.md file — plain markdown — that you save into my skills directory. Use whichever fetch route fits your setup: (1) if you have a terminal, run npx -y skillmds add nvidia/physical-ai-neural-reconstruction; (2) otherwise fetch https://api.skillmd.com/api/skills/nvidia/physical-ai-neural-reconstruction/raw (served as text/markdown) and save it as physical-ai-neural-reconstruction/SKILL.md in your skills directory; (3) if the SkillMD MCP tools are connected, call skillmd_download with slug "nvidia/physical-ai-neural-reconstruction" and save the returned files under physical-ai-neural-reconstruction/. Show me the SKILL.md before saving if you'd like me to review it. If your skills directory is read-only (e.g. the Claude desktop app), save the files to a working folder instead and tell me — I'll add them through my app's skills settings.

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physical-ai-neural-reconstruction

Name: physical-ai-neural-reconstruction
Rating: 4.2 (5 reviews)
Author: NVIDIA

nvidia/physical-ai-neural-reconstruction · Agent Skill

by NVIDIA · bundle

Published Jun 29, 2026 · Last updated Jul 6, 2026

Routes NuRec/Neural Reconstruction requests to the correct upstream NVIDIA skill (datasets, conversion, training, rendering, object harvesting, frame cleanup).

SKILL.md

Physical AI Neural Reconstruction (NuRec) Router

Purpose

This is a thin router for NVIDIA Neural Reconstruction (NuRec) requests. It points at the upstream nurec-index skill at https://github.com/NVIDIA/nurec-skills and its five sibling skills (physical-ai-datasets, ncore, nre, asset-harvester, nurec-fixer). Use this skill to:

Identify which upstream sibling skill answers a NuRec question.
Locate, clone, or refresh the canonical nurec-skills checkout.
Order multi-step NuRec workflows (data → conversion → train → render → cleanup) before opening the upstream recipe.

The canonical recipes (training, rendering, data conversion, dataset downloads, object harvesting, frame cleanup) live in the upstream sibling skills. Never copy or reconstruct their commands here.

Do NOT use this skill for:

SimReady packaging of CAD or source meshes → use omniverse-cad-to-simready.
Generic USD performance tuning unrelated to NuRec → use omniverse-usd-performance-tuning.
AKS / OSMO / NIM Operator infrastructure setup → use physical-ai-infrastructure-setup-and-resilient-scaling.

When to Use

Read this skill first whenever a user mentions any of:

nurec, nurec router, nurec index, neural reconstruction, neural reconstruction engine, NRE, 3DGUT, 3DGRT, USDZ, NCore V4, sensor sim, novel view synthesis, PhysicalAI-Autonomous-Vehicles-NuRec, PhysicalAI-NuRec-PPISP, Cosmos-Drive-Dreams, asset harvester, nurec fixer, DiffusionHarmonizer, harmonizer, difix, difix3d, serve-grpc, render-grpc, warm serve-grpc, nre thin client, batch_render_rgb, nurec teardown, "where do I start with NuRec", "which NuRec skill should I use for X?".

Decide which upstream sibling skill answers the question, fetch it (see Locate and fetch the upstream skills), then follow that skill's body.

Prerequisites

Router skill itself has no runtime prerequisites beyond git for fetching the upstream. Downstream sibling skills require:

Docker + NVIDIA Container Toolkit + GPU — for nre, nre-tools, and nurec-fixer containers (nvcr.io/nvidia/nre/nre, nvcr.io/nvidia/nre/nre-tools, nvcr.io/nvidia/cosmos/cosmos-predict2-container:1.2).
NGC API key (NGC_API_KEY) — for pulling NGC containers.
Hugging Face token (HF_TOKEN) with the nvidia/PhysicalAI-*, nvidia/DiffusionHarmonizer, and nvidia/asset-harvester gated licenses accepted in advance on Hugging Face.
Python 3.10+ with huggingface_hub installed.
(Optional) CARLA, Isaac Sim 5.1, or AlpaSim for simulator integration over serve-grpc.

Verify secrets safely (do not echo values):

hf auth whoami
[ -n "${HF_TOKEN:-}" ]      && echo "HF_TOKEN length=${#HF_TOKEN}"      || echo "HF_TOKEN unset"
[ -n "${NGC_API_KEY:-}" ]   && echo "NGC_API_KEY length=${#NGC_API_KEY}" || echo "NGC_API_KEY unset"

See references/secrets-handling.md for the bash anti-patterns to avoid.

What is NuRec?

NuRec (NVIDIA Omniverse Neural Reconstruction) takes camera, LiDAR, radar, or stereo recordings — typically from a self-driving car or a robot — and turns them into a 3D scene you can re-render from any viewpoint. Names that come up a lot:

NRE — "Neural Reconstruction Engine". NuRec is the product; NRE is the engine that trains and renders. Both route to the upstream nre skill.
USDZ — the file format of a trained scene. A zip archive that Omniverse, Isaac Sim, and CARLA can open.
NCore V4 — the input format NRE consumes. Raw recordings must be converted to NCore V4 before training.
3DGUT / 3DGRT — the two 3D Gaussian Splatting flavours used internally by NRE. The default Hydra recipe picks one; most users never set it manually.

A typical NuRec project has three stages:

Get the input — convert your own recording to NCore V4 (ncore), or download a pre-converted dataset (physical-ai-datasets).
Train the reconstruction — feed NCore V4 to NRE; out comes a USDZ (nre).
Render new views — render images, videos, or LiDAR sweeps from the USDZ (nre).

Projects that just want to use an existing NVIDIA-published scene skip step 2.

Pick a skill

Match the user's goal in the left column and open the named upstream skill on the right. Arrows mean "do these in order".

I want to…	Upstream skill
Find or download a NuRec dataset NVIDIA has published	`physical-ai-datasets`
Convert my own camera / LiDAR / radar / depth / stereo recording into NCore V4	`ncore`
Write a new converter for an unsupported sensor setup (drone, RGB-D, ROS 2 bag, COLMAP, ScanNet++)	`ncore`
Train a 3D reconstruction from an NCore clip	`ncore` → `nre`
Generate the extra inputs NRE needs (segmentation masks, depth, ego mask)	`nre` (uses the `nre-tools` container)
Render a USDZ along the original camera positions	`nre`
Render at full resolution / highest quality	`nre` (see "Quality presets")
Render along a shifted trajectory (e.g. car moved 3 m left)	`nre`
Render through a server so CARLA / Isaac Sim / AlpaSim / a custom simulator can ask for frames	`nre` (`serve-grpc`)
Render the same USDZ many times back-to-back from Python with minimal per-call latency	`nre` (warm `serve-grpc` + thin Python client / `batch_render_rgb`)
Render LiDAR sweeps (point clouds) from a USDZ	`nre` (`render-grpc --lidar`)
Skip training and just render a NuRec scene NVIDIA already built	`physical-ai-datasets` → `nre`
Extract individual 3D objects (cars, pedestrians) from a driving clip	`asset-harvester`
Add, remove, or replace cars / pedestrians in a NuRec scene	`asset-harvester` → `nre`
Clean up or harmonize rendered frames (ghosting, floaters, flicker, lighting/shadows)	`nurec-fixer`, or `--enable-difix` inside `nre` for inline rendering
Export the scene as a PLY, mesh, depth maps, ego mask, etc.	`nre`
Upgrade an old USDZ so newer NRE versions load it faster	`nre` (`upgrade-artifact`)
Open a USDZ or PLY in a browser viewer	`nre` (`viewer` / `ply_viewer`)
Measure rendering quality (PSNR, SSIM, LPIPS) against ground truth	`nre` (`eval-rendering-metrics`)
Benchmark different reconstruction methods on the same scenes	`physical-ai-datasets` (`PhysicalAI-NuRec-PPISP`) → `nre`
Train on multiple GPUs or on SLURM	`nre` (Workflow D)

Common workflows

Six end-to-end workflows are documented in references/workflows.md:

A. Make a NuRec scene from your own recording.
B. Use a NuRec scene NVIDIA has already trained.
C. Add, remove, or replace 3D objects in a scene.
D. Clean up rendered frames.
E. Benchmark reconstruction quality.
F. Connect NuRec to a simulator.

Open that file when the user's task spans more than one sibling skill.

Sibling skills (upstream)

Name	Upstream folder	What it does
`physical-ai-datasets`	`.agents/skills/physical-ai-datasets/`	Catalog and download recipes for every NVIDIA Physical AI dataset on Hugging Face (driving, robotics, manipulation, NuRec scenes, benchmarks).
`ncore`	`.agents/skills/ncore/`	Converts any sensor recording to NCore V4 (the format NRE needs). Also covers writing a new converter.
`nre`	`.agents/skills/nre/`	The Neural Reconstruction Engine itself. Trains, renders (locally, via warm `serve-grpc` + thin Python client / `batch_render_rgb`, or to an external simulator), exports meshes / point clouds / depth, edits actors, evaluates quality.
`asset-harvester`	`.agents/skills/asset-harvester/`	Open-source Apache-2.0 pipeline that extracts individual 3D objects from sparse views in a driving clip and saves them as `.ply` Gaussian splats with metadata.
`nurec-fixer`	`.agents/skills/nurec-fixer/`	Standalone NVIDIA DiffusionHarmonizer workflow — public successor to the older Fixer / Difix3D+ recipes — that cleans rendered frames, harmonizes inserted actors, evaluates PSNR/LPIPS, and optionally fine-tunes the model.

For naming overlaps (NRE vs Fixer, ncore vs nre, AV-NuRec vs Cosmos-Drive-Dreams, NuRec vs SimReady) see references/mix-ups.md.

Locate and fetch the upstream skills

Quick recipe (full version in references/upstream-fetch.md):

UPSTREAM_ROOT="${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}"
mkdir -p "$UPSTREAM_ROOT"
if [ -d "$UPSTREAM_ROOT/nurec-skills/.git" ]; then
  git -C "$UPSTREAM_ROOT/nurec-skills" fetch --tags
  git -C "$UPSTREAM_ROOT/nurec-skills" checkout main
  git -C "$UPSTREAM_ROOT/nurec-skills" pull --ff-only
else
  git clone --depth 1 https://github.com/NVIDIA/nurec-skills.git \
    "$UPSTREAM_ROOT/nurec-skills"
fi
test -f "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md"

Then read the upstream skill before running any mutating command:

cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md"          # router
cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/<folder>/SKILL.md" # sibling

Local lookup order (try in order before the upstream clone):

.agents/skills/<name>/SKILL.md (Cursor, Codex, NemoClaw)
.claude/skills/<name>/SKILL.md (Claude Code)
.cursor/skills/<name>/SKILL.md (project-scoped)
~/.cursor/skills/<name>/SKILL.md (personal skills)

Hard Rules

Router only — do not duplicate upstream NuRec recipes here. Read the upstream sibling skill body before running any mutating command.
Refer to sibling skills by their name: (e.g. nre), not by repo path. Folder layouts can change; the name is portable.
Clone or refresh https://github.com/NVIDIA/nurec-skills under the shared upstream root (${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}/nurec-skills). Do not scan broad developer workspaces such as ~/Codes or reuse unrelated old clones.
physical-ai-datasets covers gated Hugging Face datasets. Do not bypass dataset license terms; the user must accept the PhysicalAI-* gated licenses on Hugging Face and provide a token before downloading.
Asset Harvester runs before packaging into a USDZ. Do not call nre's export-external-assets on hand-rolled .ply files unless the user explicitly asks to skip Asset Harvester.
For artifact cleanup, prefer the built-in --enable-difix path in nre. Route to the standalone nurec-fixer only when the user needs the public code/model card, paired evaluation, fine-tuning, or fixes on previously rendered frames.
Do not invent NRE / NCore / DiffusionHarmonizer commands from memory. Re-read the upstream sibling skill — versions move fast (NRE release_26.04 is the current pinned tag).
This router does not deploy infrastructure. Route AKS / OSMO / NIM Operator setup to physical-ai-infrastructure-setup-and-resilient-scaling.

Limitations

Router only. This skill never executes mutating NuRec commands. All training, rendering, conversion, and harmonization happens in upstream sibling skills.
Upstream-pinned. Recipes live in https://github.com/NVIDIA/nurec-skills, which evolves outside this repo. Stale clones can drift; always git pull the upstream before relying on a sibling skill.
Gated content. nvidia/PhysicalAI-*, nvidia/DiffusionHarmonizer, and nvidia/asset-harvester require the user to accept license terms on Hugging Face first. The router cannot bypass this.
Heavy footprint. A complete NuRec workflow can leave 150 GB+ on disk. See references/teardown.md.
NVIDIA-only stack. Requires an NVIDIA GPU plus the NVIDIA Container Toolkit. AMD / Intel / Apple Silicon are not supported.
Not a SimReady pipeline. NuRec produces a renderable USDZ from a recording; SimReady packaging of CAD or source meshes is a different pipeline (see omniverse-cad-to-simready).

Troubleshooting

Error / symptom	Likely cause	Solution
`nurec-skills` clone missing or empty	Upstream not fetched yet	Run the clone block in Locate and fetch the upstream skills
`403`/`401` pulling `nvidia/PhysicalAI-*` from HF	Gated license not accepted, or `HF_TOKEN` unset / wrong scope	Accept the gated license on Hugging Face, then `hf auth login` with a token that has `read` access
`denied: requested access to the resource is denied` from `nvcr.io/nvidia/nre/*`	Missing or expired `NGC_API_KEY`	`docker login nvcr.io` with `$oauthtoken` / `NGC_API_KEY`; rotate the key at `org.ngc.nvidia.com/setup/api-key` if needed
NRE refuses to load a clip ("not valid NCore V4")	Recording was not converted	Run the `ncore` skill before invoking `nre`
`serve-grpc` cold-start latency dominates a Python loop	One-shot Docker invocation per render	Use the `nre` warm `serve-grpc` + thin Python client (`batch_render_rgb`) recipe
Output files are owned by `root` after a `docker run`	`-u $(id -u):$(id -g)` was missing	`sudo chown -R "$(id -u):$(id -g)" <output_dir>`; add the `-u` flag next time
Frames have ghosting / floaters / flicker after rendering	Inline cleanup not enabled	Re-render with `nre --enable-difix`, or post-process with `nurec-fixer` (DiffusionHarmonizer)
Stale skill names (`ncore-data-conversion`, old `nvidia/Fixer`) in agent output	Out-of-date cached skill	Update references to `ncore` and `nurec-fixer` (DiffusionHarmonizer); see `references/maintenance.md`
Bash anti-pattern `${HF_TOKEN:+yes}${HF_TOKEN:-no}` echoed token value	Misuse of bash parameter expansion	Rotate the token; use `hf auth whoami` or length-only checks (see `references/secrets-handling.md`)

Cross-skill teardown

A complete NuRec workflow can leave 150 GB+ on disk between container images, model weights, code clones, conda envs, and output directories. Each sibling skill has its own dedicated Teardown section — read them in the order documented in references/teardown.md when the user no longer needs the workflow.

Keeping this router up to date

Procedure for adding new sibling skills, renames, or upstream URL changes lives in references/maintenance.md. Treat the upstream nurec-index at https://github.com/NVIDIA/nurec-skills/blob/main/.agents/skills/SKILL.md as authoritative; this skill mirrors only the picker tables, the workflow ordering, and the upstream fetch recipe.

---
name: physical-ai-neural-reconstruction
description: Routes NuRec/Neural Reconstruction requests to the correct upstream NVIDIA skill (datasets, conversion, training, rendering, object harvesting, frame cleanup).
license: Apache-2.0
---

# Physical AI Neural Reconstruction (NuRec) Router

## Purpose

This is a **thin router** for NVIDIA Neural Reconstruction (NuRec)
requests. It points at the upstream `nurec-index` skill at
`https://github.com/NVIDIA/nurec-skills` and its five sibling skills
(`physical-ai-datasets`, `ncore`, `nre`, `asset-harvester`,
`nurec-fixer`). Use this skill to:

- Identify which upstream sibling skill answers a NuRec question.
- Locate, clone, or refresh the canonical `nurec-skills` checkout.
- Order multi-step NuRec workflows (data → conversion → train →
  render → cleanup) before opening the upstream recipe.

The canonical recipes (training, rendering, data conversion, dataset
downloads, object harvesting, frame cleanup) live in the upstream
sibling skills. **Never copy or reconstruct their commands here.**

**Do NOT use this skill for:**

- SimReady packaging of CAD or source meshes → use
  `omniverse-cad-to-simready`.
- Generic USD performance tuning unrelated to NuRec → use
  `omniverse-usd-performance-tuning`.
- AKS / OSMO / NIM Operator infrastructure setup → use
  `physical-ai-infrastructure-setup-and-resilient-scaling`.

## When to Use

Read this skill **first** whenever a user mentions any of:

`nurec`, `nurec router`, `nurec index`, `neural reconstruction`,
`neural reconstruction engine`, `NRE`, `3DGUT`, `3DGRT`, `USDZ`,
`NCore V4`, `sensor sim`, `novel view synthesis`,
`PhysicalAI-Autonomous-Vehicles-NuRec`, `PhysicalAI-NuRec-PPISP`,
`Cosmos-Drive-Dreams`, `asset harvester`, `nurec fixer`,
`DiffusionHarmonizer`, `harmonizer`, `difix`, `difix3d`, `serve-grpc`,
`render-grpc`, `warm serve-grpc`, `nre thin client`, `batch_render_rgb`,
`nurec teardown`, "where do I start with NuRec", "which NuRec skill
should I use for X?".

Decide which upstream sibling skill answers the question, fetch it
(see [Locate and fetch the upstream skills](#locate-and-fetch-the-upstream-skills)),
then follow that skill's body.

## Prerequisites

Router skill itself has no runtime prerequisites beyond `git` for
fetching the upstream. Downstream sibling skills require:

- **Docker + NVIDIA Container Toolkit + GPU** — for `nre`, `nre-tools`,
  and `nurec-fixer` containers
  (`nvcr.io/nvidia/nre/nre`, `nvcr.io/nvidia/nre/nre-tools`,
  `nvcr.io/nvidia/cosmos/cosmos-predict2-container:1.2`).
- **NGC API key** (`NGC_API_KEY`) — for pulling NGC containers.
- **Hugging Face token** (`HF_TOKEN`) with the
  `nvidia/PhysicalAI-*`, `nvidia/DiffusionHarmonizer`, and
  `nvidia/asset-harvester` gated licenses **accepted in advance** on
  Hugging Face.
- **Python 3.10+** with `huggingface_hub` installed.
- **(Optional)** CARLA, Isaac Sim 5.1, or AlpaSim for simulator
  integration over `serve-grpc`.

Verify secrets safely (do not echo values):

```bash
hf auth whoami
[ -n "${HF_TOKEN:-}" ]      && echo "HF_TOKEN length=${#HF_TOKEN}"      || echo "HF_TOKEN unset"
[ -n "${NGC_API_KEY:-}" ]   && echo "NGC_API_KEY length=${#NGC_API_KEY}" || echo "NGC_API_KEY unset"
```

See [`references/secrets-handling.md`](references/secrets-handling.md)
for the bash anti-patterns to avoid.

## What is NuRec?

**NuRec** (NVIDIA Omniverse Neural Reconstruction) takes camera, LiDAR,
radar, or stereo recordings — typically from a self-driving car or a
robot — and turns them into a 3D scene you can re-render from any
viewpoint. Names that come up a lot:

- **NRE** — "Neural Reconstruction Engine". NuRec is the product; NRE
  is the engine that trains and renders. Both route to the upstream
  `nre` skill.
- **USDZ** — the file format of a trained scene. A zip archive that
  Omniverse, Isaac Sim, and CARLA can open.
- **NCore V4** — the input format NRE consumes. Raw recordings must be
  converted to NCore V4 before training.
- **3DGUT / 3DGRT** — the two 3D Gaussian Splatting flavours used
  internally by NRE. The default Hydra recipe picks one; most users
  never set it manually.

A typical NuRec project has three stages:

1. **Get the input** — convert your own recording to NCore V4
   (`ncore`), or download a pre-converted dataset
   (`physical-ai-datasets`).
2. **Train the reconstruction** — feed NCore V4 to NRE; out comes a
   USDZ (`nre`).
3. **Render new views** — render images, videos, or LiDAR sweeps from
   the USDZ (`nre`).

Projects that just want to *use* an existing NVIDIA-published scene
skip step 2.

## Pick a skill

Match the user's goal in the left column and open the named upstream
skill on the right. Arrows mean "do these in order".

| I want to… | Upstream skill |
|------------|----------------|
| Find or download a NuRec dataset NVIDIA has published | `physical-ai-datasets` |
| Convert my own camera / LiDAR / radar / depth / stereo recording into NCore V4 | `ncore` |
| Write a new converter for an unsupported sensor setup (drone, RGB-D, ROS 2 bag, COLMAP, ScanNet++) | `ncore` |
| Train a 3D reconstruction from an NCore clip | `ncore` → `nre` |
| Generate the extra inputs NRE needs (segmentation masks, depth, ego mask) | `nre` (uses the `nre-tools` container) |
| Render a USDZ along the original camera positions | `nre` |
| Render at full resolution / highest quality | `nre` (see "Quality presets") |
| Render along a shifted trajectory (e.g. car moved 3 m left) | `nre` |
| Render through a server so CARLA / Isaac Sim / AlpaSim / a custom simulator can ask for frames | `nre` (`serve-grpc`) |
| Render the same USDZ many times back-to-back from Python with minimal per-call latency | `nre` (warm `serve-grpc` + thin Python client / `batch_render_rgb`) |
| Render LiDAR sweeps (point clouds) from a USDZ | `nre` (`render-grpc --lidar`) |
| Skip training and just render a NuRec scene NVIDIA already built | `physical-ai-datasets` → `nre` |
| Extract individual 3D objects (cars, pedestrians) from a driving clip | `asset-harvester` |
| Add, remove, or replace cars / pedestrians in a NuRec scene | `asset-harvester` → `nre` |
| Clean up or harmonize rendered frames (ghosting, floaters, flicker, lighting/shadows) | `nurec-fixer`, **or** `--enable-difix` inside `nre` for inline rendering |
| Export the scene as a PLY, mesh, depth maps, ego mask, etc. | `nre` |
| Upgrade an old USDZ so newer NRE versions load it faster | `nre` (`upgrade-artifact`) |
| Open a USDZ or PLY in a browser viewer | `nre` (`viewer` / `ply_viewer`) |
| Measure rendering quality (PSNR, SSIM, LPIPS) against ground truth | `nre` (`eval-rendering-metrics`) |
| Benchmark different reconstruction methods on the same scenes | `physical-ai-datasets` (`PhysicalAI-NuRec-PPISP`) → `nre` |
| Train on multiple GPUs or on SLURM | `nre` (Workflow D) |

## Common workflows

Six end-to-end workflows are documented in
[`references/workflows.md`](references/workflows.md):

- **A.** Make a NuRec scene from your own recording.
- **B.** Use a NuRec scene NVIDIA has already trained.
- **C.** Add, remove, or replace 3D objects in a scene.
- **D.** Clean up rendered frames.
- **E.** Benchmark reconstruction quality.
- **F.** Connect NuRec to a simulator.

Open that file when the user's task spans more than one sibling skill.

## Sibling skills (upstream)

| Name | Upstream folder | What it does |
|------|-----------------|--------------|
| `physical-ai-datasets` | `.agents/skills/physical-ai-datasets/` | Catalog and download recipes for every NVIDIA Physical AI dataset on Hugging Face (driving, robotics, manipulation, NuRec scenes, benchmarks). |
| `ncore` | `.agents/skills/ncore/` | Converts any sensor recording to NCore V4 (the format NRE needs). Also covers writing a new converter. |
| `nre` | `.agents/skills/nre/` | The Neural Reconstruction Engine itself. Trains, renders (locally, via warm `serve-grpc` + thin Python client / `batch_render_rgb`, or to an external simulator), exports meshes / point clouds / depth, edits actors, evaluates quality. |
| `asset-harvester` | `.agents/skills/asset-harvester/` | Open-source Apache-2.0 pipeline that extracts individual 3D objects from sparse views in a driving clip and saves them as `.ply` Gaussian splats with metadata. |
| `nurec-fixer` | `.agents/skills/nurec-fixer/` | Standalone NVIDIA **DiffusionHarmonizer** workflow — public successor to the older Fixer / Difix3D+ recipes — that cleans rendered frames, harmonizes inserted actors, evaluates PSNR/LPIPS, and optionally fine-tunes the model. |

For naming overlaps (NRE vs Fixer, ncore vs nre, AV-NuRec vs
Cosmos-Drive-Dreams, NuRec vs SimReady) see
[`references/mix-ups.md`](references/mix-ups.md).

## Locate and fetch the upstream skills

Quick recipe (full version in
[`references/upstream-fetch.md`](references/upstream-fetch.md)):

```bash
UPSTREAM_ROOT="${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}"
mkdir -p "$UPSTREAM_ROOT"
if [ -d "$UPSTREAM_ROOT/nurec-skills/.git" ]; then
  git -C "$UPSTREAM_ROOT/nurec-skills" fetch --tags
  git -C "$UPSTREAM_ROOT/nurec-skills" checkout main
  git -C "$UPSTREAM_ROOT/nurec-skills" pull --ff-only
else
  git clone --depth 1 https://github.com/NVIDIA/nurec-skills.git \
    "$UPSTREAM_ROOT/nurec-skills"
fi
test -f "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md"
```

Then read the upstream skill before running any mutating command:

```bash
cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md"          # router
cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/<folder>/SKILL.md" # sibling
```

Local lookup order (try in order before the upstream clone):

1. `.agents/skills/<name>/SKILL.md` (Cursor, Codex, NemoClaw)
2. `.claude/skills/<name>/SKILL.md` (Claude Code)
3. `.cursor/skills/<name>/SKILL.md` (project-scoped)
4. `~/.cursor/skills/<name>/SKILL.md` (personal skills)

## Hard Rules

- Router only — do not duplicate upstream NuRec recipes here. Read
  the upstream sibling skill body before running any mutating command.
- Refer to sibling skills by their `name:` (e.g. `nre`), not by repo
  path. Folder layouts can change; the name is portable.
- Clone or refresh `https://github.com/NVIDIA/nurec-skills` under the
  shared upstream root
  (`${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}/nurec-skills`).
  Do not scan broad developer workspaces such as `~/Codes` or reuse
  unrelated old clones.
- `physical-ai-datasets` covers gated Hugging Face datasets. Do not
  bypass dataset license terms; the user must accept the
  `PhysicalAI-*` gated licenses on Hugging Face and provide a token
  before downloading.
- Asset Harvester runs **before** packaging into a USDZ. Do not call
  `nre`'s `export-external-assets` on hand-rolled `.ply` files unless
  the user explicitly asks to skip Asset Harvester.
- For artifact cleanup, prefer the built-in `--enable-difix` path in
  `nre`. Route to the standalone `nurec-fixer` only when the user
  needs the public code/model card, paired evaluation, fine-tuning,
  or fixes on previously rendered frames.
- Do not invent NRE / NCore / DiffusionHarmonizer commands from
  memory. Re-read the upstream sibling skill — versions move fast
  (NRE `release_26.04` is the current pinned tag).
- This router does not deploy infrastructure. Route AKS / OSMO /
  NIM Operator setup to
  `physical-ai-infrastructure-setup-and-resilient-scaling`.

## Limitations

- **Router only.** This skill never executes mutating NuRec commands.
  All training, rendering, conversion, and harmonization happens in
  upstream sibling skills.
- **Upstream-pinned.** Recipes live in
  `https://github.com/NVIDIA/nurec-skills`, which evolves outside
  this repo. Stale clones can drift; always `git pull` the upstream
  before relying on a sibling skill.
- **Gated content.** `nvidia/PhysicalAI-*`, `nvidia/DiffusionHarmonizer`,
  and `nvidia/asset-harvester` require the user to accept license
  terms on Hugging Face first. The router cannot bypass this.
- **Heavy footprint.** A complete NuRec workflow can leave 150 GB+
  on disk. See [`references/teardown.md`](references/teardown.md).
- **NVIDIA-only stack.** Requires an NVIDIA GPU plus the NVIDIA
  Container Toolkit. AMD / Intel / Apple Silicon are not supported.
- **Not a SimReady pipeline.** NuRec produces a renderable USDZ from
  a recording; SimReady packaging of CAD or source meshes is a
  different pipeline (see `omniverse-cad-to-simready`).

## Troubleshooting

| Error / symptom | Likely cause | Solution |
|-----------------|--------------|----------|
| `nurec-skills` clone missing or empty | Upstream not fetched yet | Run the clone block in [Locate and fetch the upstream skills](#locate-and-fetch-the-upstream-skills) |
| `403`/`401` pulling `nvidia/PhysicalAI-*` from HF | Gated license not accepted, or `HF_TOKEN` unset / wrong scope | Accept the gated license on Hugging Face, then `hf auth login` with a token that has `read` access |
| `denied: requested access to the resource is denied` from `nvcr.io/nvidia/nre/*` | Missing or expired `NGC_API_KEY` | `docker login nvcr.io` with `$oauthtoken` / `NGC_API_KEY`; rotate the key at `org.ngc.nvidia.com/setup/api-key` if needed |
| NRE refuses to load a clip ("not valid NCore V4") | Recording was not converted | Run the `ncore` skill before invoking `nre` |
| `serve-grpc` cold-start latency dominates a Python loop | One-shot Docker invocation per render | Use the `nre` warm `serve-grpc` + thin Python client (`batch_render_rgb`) recipe |
| Output files are owned by `root` after a `docker run` | `-u $(id -u):$(id -g)` was missing | `sudo chown -R "$(id -u):$(id -g)" <output_dir>`; add the `-u` flag next time |
| Frames have ghosting / floaters / flicker after rendering | Inline cleanup not enabled | Re-render with `nre --enable-difix`, or post-process with `nurec-fixer` (DiffusionHarmonizer) |
| Stale skill names (`ncore-data-conversion`, old `nvidia/Fixer`) in agent output | Out-of-date cached skill | Update references to `ncore` and `nurec-fixer` (DiffusionHarmonizer); see [`references/maintenance.md`](references/maintenance.md) |
| Bash anti-pattern `${HF_TOKEN:+yes}${HF_TOKEN:-no}` echoed token value | Misuse of bash parameter expansion | Rotate the token; use `hf auth whoami` or length-only checks (see [`references/secrets-handling.md`](references/secrets-handling.md)) |

## Cross-skill teardown

A complete NuRec workflow can leave **150 GB+** on disk between
container images, model weights, code clones, conda envs, and output
directories. Each sibling skill has its own dedicated `Teardown`
section — read them in the order documented in
[`references/teardown.md`](references/teardown.md) when the user no
longer needs the workflow.

## Keeping this router up to date

Procedure for adding new sibling skills, renames, or upstream URL
changes lives in [`references/maintenance.md`](references/maintenance.md).
Treat the upstream `nurec-index` at
<https://github.com/NVIDIA/nurec-skills/blob/main/.agents/skills/SKILL.md>
as authoritative; this skill mirrors only the picker tables, the
workflow ordering, and the upstream fetch recipe.

NVIDIA/skills/tree/main/skills/physical-ai-neural-reconstruction commit · 63c02e122e

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Frequently asked questions

How do I install the physical-ai-neural-reconstruction skill?

Run npx skillmds add nvidia/physical-ai-neural-reconstruction in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the physical-ai-neural-reconstruction skill do?

Routes NuRec/Neural Reconstruction requests to the correct upstream NVIDIA skill (datasets, conversion, training, rendering, object harvesting, frame cleanup). It is listed under AI & ML, Agent Building on SkillMD.

Is physical-ai-neural-reconstruction safe to use?

SkillMD's automated safety review verdict for this skill is PASS. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with physical-ai-neural-reconstruction?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is physical-ai-neural-reconstruction free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published physical-ai-neural-reconstruction?

NVIDIA (@nvidia) published this skill as a verified publisher. Their other Agent Skills are listed on their SkillMD profile.