Datasets:

AIGeeksGroup
/

PhysicalWM

<div align="center">

# OVOW: One Video, One World

### Turning Monocular Video into Physical 4D Scenes

<a href="https://arxiv.org/abs/2606.31388"><img src="https://img.shields.io/badge/ECCV-2026-1a73e8" alt="ECCV 2026"></a>
<a href="https://arxiv.org/abs/2606.31388"><img src="https://img.shields.io/badge/arXiv-2606.31388-b31b1b?logo=arxiv&logoColor=white" alt="arXiv"></a>
<a href="https://OneVideoOneWorld.github.io/"><img src="https://img.shields.io/badge/Project_Page-OVOW-green" alt="Project Page"></a>
<a href="https://arxiv.org/pdf/2606.31388"><img src="https://img.shields.io/badge/Paper-PDF-blue" alt="Paper PDF"></a>
<a href="NOTICE"><img src="https://img.shields.io/badge/License-Mixed_(research--only)-yellow" alt="Mixed licenses: research-only release"></a>

[English](README.md) | [中文](README_zh.md)

<img src="assets/teaser.jpg" alt="OVOW teaser: monocular video to instance-level, simulation-ready 4D mesh scenes" width="92%">

</div>

**OVOW** is the first **training-free** system that reconstructs **instance-level, simulation-ready**
4D mesh scenes from a single monocular video (or a single image): per-object 3D meshes + poses +
deformation animation, assembled into a full-scene animated GLB with rendered comparison videos.
Heavy models (SAM3 / Hi3DGen / FoundationPose / RoMaV2) support **resident serving**: loaded once
for an entire batch.

> ⚠️ **Non-commercial research use only.** OVOW's own code is MIT-licensed, but this release bundles
> third-party components under non-commercial licenses (NVIDIA FoundationPose, CC-BY-NC SpaTrackerV2,
> CC-BY-NC-SA Motion324, Meta SAM License sam3, etc.). By using it you agree to each component's
> license — see [NOTICE](NOTICE) and [THIRD_PARTY.md](THIRD_PARTY.md).
> Consequently, the assembled repository is a **source-available research release**, not an
> OSI-approved open-source distribution.

## Reproduce (from zero to results)

**Prerequisites**: Linux + NVIDIA GPU (≥40GB VRAM recommended, driver ≥550) · CUDA Toolkit 12.1+
(with `nvcc`) · conda · ~120GB disk.

```bash
# 0) Clone
git clone https://github.com/SparcAI-Inc/OVOW.git && cd OVOW
# 1) Create the 3 conda envs (installs torch + deps + compiles CUDA extensions;
#    the slowest step, ~30-60 min)
bash environment/create_envs.sh        # prints the 3 interpreter paths to paste into .env
# 2) Machine config
cp .env.example .env                   # ① paste the 3 python paths printed above
                                       # ② set DASHSCOPE_API_KEY, or set QWEN_BACKEND="local" (offline)
# 3) Install blender (hard dependency of the assembly/rendering steps)
sudo apt install -y blender            # or the official portable build → set BLENDER_BIN in .env
# 4) Localize paths (expands the repo's placeholders with your .env; one-shot —
#    to change .env later, `git checkout` the tree first, then rerun)
bash setup.sh
# 5) Download weights (~55GB, fully automatic; SAM3 is a gated HF repo —
#    `huggingface-cli login` and accept its terms on the web page first)
bash download_ckpts.sh
# 6) Reproduce the bundled examples (see below)
bash examples/smoke_test.sh          # single image, short chain, ~3-5 min
bash examples/smoke_test_video.sh    # single video, full 4D chain, ~19-22 min
```

If a step fails, see [Troubleshooting](#troubleshooting) below. For the per-environment
torch/CUDA version table and the manual, step-by-step install commands that
`create_envs.sh` automates, see [environment/README.md](environment/README.md).

### What "reproduced" means here

The reproduction target is the bundled `examples/` — running both of them through to
their artifacts is the bar for a working installation. Both scripts start with a
preflight that names exactly what is missing (Qwen backend / blender / conda
interpreters / SAM3 and Motion324 weights) instead of failing halfway through, and both
end by asserting that the key artifacts exist and are non-empty — a zero exit code alone
does not count as reproduced.

| Script | Input | Steps | Artifacts under `outputs/` |
|---|---|---|---|
| `examples/smoke_test.sh` | `examples/images/desk_banana.png` | 1 2 6 7 14 17 18 (no deform) | `smoke/image_pipeline_<ts>/desk_banana/` |
| `examples/smoke_test_video.sh` | `examples/videos/instance_000008.mp4` (768×768, 108 frames) | full chain incl. deform | `smoke_video/ws/instance_000008_<ts>_<key>/` |

Both assert these three artifacts (`.png` for the image chain, `.mp4` for the video chain):

```
scene_anim_blender_physics_with_deform.glb                          # final full-scene GLB
fullscene_deform_objseq_white_left_rgb_right_split10_h264.{png,mp4} # white-model vs input
fullscene_deform_objseq_textured_left_rgb_right_split10_h264.{png,mp4} # textured vs input
```

`smoke_test_video.sh` also takes a video path, so you can point it at your own clip:
`bash examples/smoke_test_video.sh /path/to/your.mp4`.

Note that the generative steps do not run with a fixed seed, so meshes and renders differ
between runs. The bar is that the chain completes and produces the artifacts above, not
bit-identical output.

## Usage

```bash
# A single image
bash scripts/run_image.sh --image /path/x.png --out ./outputs
# A single video
bash scripts/run_video.sh --name eagle --video /path/eagle.mp4 --out ./outputs
# All videos in a folder (resident serving: models loaded once for the whole batch) — recommended at scale
bash scripts/run_video_batch.sh --video-dir /path/videos --gpu 0 --out ./outputs
# Mixed images+videos, multi-GPU, resumable (100k-scale)
bash scripts/run_batch.sh --in /path/inputs --gpus 0,1,2,3 --out ./outputs
```

Outputs: videos under `<out>/ws/<name>_<timestamp>_<key>/`, single images under
`<out>/image_pipeline_<timestamp>/<name>/`:
- `scene_anim_blender_physics_with_deform.glb` — final full-scene animated GLB
- `fullscene_deform_*_h264.mp4` — white-model / textured rendered comparison videos

## Benchmark

`benchmark/` holds **OVOW-Bench**, the evaluation toolkit used in the paper. It
scores already-reconstructed scenes and reports Scene-IoU (AABB and OBB),
Object-IoU after Hungarian matching with an object-count penalty, a photometric
loss, and an N-CLIP visual metric. Geometry is compared after the alignment the
protocol specifies; visual metrics use GT-camera renders.

The toolkit ships evaluator code and protocol metadata only — no ground truth,
results, or predictions. The data (`OVOW_Bench_Data`) is published separately on
Hugging Face. It needs its own conda environment plus Blender and a local CLIP
checkpoint, so it is deliberately kept apart from the three pipeline envs:

```bash
conda env create -f benchmark/environment.yml && conda activate ovow-bench
python benchmark/scripts/check_env.py
# unit tests need neither Blender nor CLIP weights
cd benchmark && PYTHONDONTWRITEBYTECODE=1 python -m unittest discover -s tests
```

Protocol, submission layout, and the full evaluator invocation are documented in
[benchmark/README.md](benchmark/README.md) and
[benchmark/docs/EVALUATION_PROTOCOL.md](benchmark/docs/EVALUATION_PROTOCOL.md).

## How it works

<div align="center">
<img src="assets/pipeline.png" alt="OVOW pipeline: VLM-guided scene decomposition → per-instance mesh reconstruction → scale & orientation recovery → per-frame pose tracking" width="95%">
</div>

| Stage | What it does | Where it runs |
|---|---|---|
| Labeling | Qwen object labeling + static/rigid/deform classification | DashScope API or local Qwen3-VL |
| Segmentation | SAM3 per-frame masks | sam3 env (resident sam3-server in batch mode) |
| Deformable meshes | Motion324 + Hunyuan | motion env |
| Static/rigid reconstruction | Hi3DGen meshes + FoundationPose poses + RoMaV2 scale recovery | opg env (resident opg-server in batch mode) |
| Deformable poses | frame_track per-frame poses | opg env (same as above) |
| Assembly / rendering | Full-scene GLB export + rendered comparisons | blender |
### Pipeline steps
The entry scripts accept an explicit list of step numbers as trailing arguments
(`bash scripts/run_video.sh --name eagle --video x.mp4 1 2 3 4 5 6 7 8 10 13 14 17 18`).
With no steps given, `run_video.sh` runs the full chain and `run_image.sh` runs the
image-only subset. Steps 15/16/19 are optional and off by default. Steps 15 and 19
additionally need components this repository does not ship and you fetch yourself,
one of them under academic-use-only terms — see the optional-components table in
[THIRD_PARTY.md](THIRD_PARTY.md) before enabling them.

| step | What it does | env / service |
|---|---|---|
| 1 | Qwen object labeling + static/rigid/deform classification | API or local Qwen3-VL |
| 2 | SAM3 per-frame masks | sam3 env (sam3-server in batch mode) |
| 3 | Build deform inputs (object-centered video) | opg (light) |
| 4 | Deformable mesh generation | Motion324 (motion env) |
| 5 | Scan Motion324 outputs → manifest | light |
| 6 | Static/rigid reconstruction (mesh + pose) | **opg-server (resident)** |
| 7 | Ground-plane fitting | opg (geometry) |
| 8 | Deformable poses (frame_track) | **opg-server (resident)** |
| 10 | Merge full-scene mesh sequence | blender |
| 13 / 14 | Export full-scene GLB (pre-physics / physics) | blender |
| 17 / 18 | Render white-model / textured comparison videos | blender |
Default reconstruction backbone is `uplegacy` = legacy/Hi3DGen generation plus the
upgraded pose chain (romav2 matching + zero-iteration scale recovery + all models
resident in-process). It needs RoMaV2 and the Hi3DGen / SpaTrackerV2 / FoundationPose
weights (all covered by step 5 above) and does **not** need TRELLIS2/CuMesh — those are
only for `--recon-backbone trellis2`.
Runtime is ~19–22 min per video end-to-end, almost entirely compute (step 6 ~6 min +
deform ~9–11 min + rendering); model loading is a one-off ~1–3 min service startup.
### Resident serving (batch mode)
`run_video_batch.sh` and `run_batch.sh` start and stop opg-server + sam3-server
automatically, each with its own queue — nothing to configure. Heavy models
(SAM3, opg core, RoMaV2, FoundationPose) are then loaded once for the whole batch
instead of once per video; Motion324 + Hunyuan (step 4) still load per video.
Resident serving pays off across many videos — for a single video it is a wash,
so single runs (`run_image.sh` / `run_video.sh`) stay in-process by default.

To manage the services manually across several batches:

```bash
bash scripts/servers/opg_server.sh  ensure --gpu 0   # ensure = start only if not already running
bash scripts/servers/opg_server.sh  status
bash scripts/servers/opg_server.sh  stop
bash scripts/servers/sam3_server.sh ensure --gpu 0
```

To make a *single* run reuse manually started services, export the queues explicitly.
The engine treats "queue variable non-empty" as "use the server" and does **not**
health-check it, so exporting a queue with no server running will hang until timeout:

```bash
export OPG_SERVER_QUEUE=/tmp/opg_server_main_q SAM3_SERVER_QUEUE=/tmp/sam3_server_q
bash scripts/run_video.sh --name eagle --video /path/eagle.mp4
```

### Key environment variables

All have sensible defaults in [configs/paths.sh](configs/paths.sh); override in `.env`.

| Variable | Effect |
|---|---|
| `OPG_SERVER_QUEUE` / `SAM3_SERVER_QUEUE` | route step 6/8 and step 2 through the resident services |
| `OPG_MATCHER=romav2` | use RoMaV2 dense matching for scale recovery |
| `OPG_SCALE_REFINE_ITERS=0` | zero-iteration scale recovery (~5x faster; converges on the first pass under romav2) |
| `OPG_ROMAV2_INPROC=1` | keep RoMaV2 resident in-process |
| `OPG_FP_RESIDENT=1` | keep the FoundationPose predictor as an in-process singleton |
| `QWEN_BACKEND=local` | run step 1 labeling on a local Qwen3-VL instead of the DashScope API |
| `HF_ENDPOINT=https://hf-mirror.com` | mirror for weight downloads |

## Troubleshooting

**Environment build (step 1) — the most likely thing to need per-platform tuning.**
The `opg` env compiles extensions on the machine (flash-attn / pytorch3d /
FoundationPose). `create_envs.sh` handles each env independently and can be re-run
per env (`bash environment/create_envs.sh opg`); the version table and the equivalent
manual commands are in [environment/README.md](environment/README.md).

- `nvcc not found` → install CUDA Toolkit 12.1+ and put it on `PATH`.
- arch/ABI mismatch → confirm torch is the cu121 wheel and `nvcc -V` is ≥ 12.1.
- flash-attn compiles too slowly or fails → use an official prebuilt wheel matching cu121 + torch 2.4.

**No DashScope account (step 2).** Set `QWEN_BACKEND="local"` in `.env` to run step 1
on a local Qwen3-VL. The image path pulls `Qwen/Qwen3-VL-8B-Instruct` from HF
automatically; you can also predownload with
`masking/download_qwen3_vl_8b_hf_mirror.sh` and point at it with `--qwen-model /path`.

**Placeholder expansion is one-shot (step 4).** `setup.sh` runs `configure.sh`, which
expands `@ENGINE_DIR@`, `@OPG_PY@`, `@BLENDER_BIN@` and friends into the paths from
your `.env`. Once expanded the tokens are consumed, so re-running it after editing
`.env` will not update them. To reconfigure, restore the tree first:

```bash
git checkout -- engine/ third_party/ masking/ && bash configure.sh
```

**Weights (step 5).**
- `facebook/sam3` is a gated HF repo: `huggingface-cli login` and accept its terms on the model page first.
- RoMaV2 weights need no download — torch.hub fetches them on first run.
- TRELLIS weights are only needed for `--recon-backbone trellis2`: `OVOW_DL_TRELLIS2=1 bash download_ckpts.sh` (+~20GB).
- The script creates two symlinks the engine expects: `engine/checkpoints/OnePoseViaGen → ckpts/OnePoseViaGen` and `third_party/sam3/checkpoints/sam3/sam3.pt → ckpts/sam3/sam3.pt`.

**Runtime errors.**
- `server not ready` → the service is still loading (opg takes ~1–3 min); check the service log in the queue directory.
- `motion manifest missing` (step 8) → step 5 did not run; make sure your step list includes `5`.
- `API key empty` → check that `APIKEY_FILE` exists and contains `sk-...`, or switch to offline labeling with `QWEN_BACKEND=local`.
- Services will not stop → `bash scripts/servers/opg_server.sh stop`, or `pkill -f opg_server.py`.

## Citation

If you find OVOW useful for your research, please cite:

```bibtex
@inproceedings{chen2026ovow,
  title     = {One Video, One World: Turning Monocular Video into Physical 4D Scenes},
  author    = {Chen, Junhao and Zhang, Boran and Chen, Mingjin and Zhang, Henghaofan and
               Zhang, Saining and Zhu, Congcong and Zhao, Hao and Huang, Ruqi and
               Li, Zhihao and Wang, Yufei},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```

## Acknowledgements

OVOW is an orchestration framework built on top of many excellent publicly released research works.
We sincerely thank the authors of:

**Core engine**
- [OnePoseviaGen](https://github.com/GZWSAMA/OnePoseviaGen) (*One View, Many Worlds*) — the
  reconstruction engine of this project; OVOW's name is also a tribute to it.

**Works integrated by the engine / depended on by this project**
- [FoundationPose](https://github.com/NVlabs/FoundationPose) — 6-DoF object pose estimation & tracking (NVIDIA)
- [SpaTrackerV2 / SpatialTrackerV2](https://github.com/henry123-boy/SpaTrackerV2) — 3D point tracking
- [Hunyuan3D-2](https://huggingface.co/Tencent-Hunyuan/Hunyuan3D-2) — Hi3DGen 3D generation (Tencent Hunyuan)
- [TRELLIS](https://github.com/microsoft/TRELLIS) — 3D generative representation (Microsoft)
- [Depth-Anything-3 (DA3)](https://github.com/DepthAnything) — monocular depth
- [SAM 2](https://github.com/facebookresearch/sam2) / [SAM 3](https://github.com/facebookresearch/sam3) — segmentation (Meta)
- [Motion324](https://github.com/Inception3D/Motion324) — deformable mesh animation
- [RoMaV2](https://github.com/Parskatt/RoMaV2) — dense feature matching (scale recovery / PnP correction)
- [Amodal3R](https://github.com/Sm0kyWu/Amodal3R) — occlusion-aware 3D reconstruction
- [BundleSDF](https://github.com/NVlabs/BundleSDF) — pose refinement (NVIDIA)
- [UniDepth](https://github.com/lpiccinelli-eth/UniDepth) — metric depth estimation (ETH)
- [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL) — multimodal labeling (Alibaba)

Per-component licenses and provenance: [THIRD_PARTY.md](THIRD_PARTY.md).

## License

OVOW-authored code is released under the **MIT** license (see [LICENSE](LICENSE)).
However, third-party components bundled in this release carry **non-commercial** licenses,
so the assembled repository uses **mixed licenses** and is for
**non-commercial research / evaluation use only** —
see [NOTICE](NOTICE) and [THIRD_PARTY.md](THIRD_PARTY.md).

	<div align="center">

	# OVOW: One Video, One World

	### Turning Monocular Video into Physical 4D Scenes

	<a href="https://arxiv.org/abs/2606.31388"><img src="https://img.shields.io/badge/ECCV-2026-1a73e8" alt="ECCV 2026"></a>
	<a href="https://arxiv.org/abs/2606.31388"><img src="https://img.shields.io/badge/arXiv-2606.31388-b31b1b?logo=arxiv&logoColor=white" alt="arXiv"></a>
	<a href="https://OneVideoOneWorld.github.io/"><img src="https://img.shields.io/badge/Project_Page-OVOW-green" alt="Project Page"></a>
	<a href="https://arxiv.org/pdf/2606.31388"><img src="https://img.shields.io/badge/Paper-PDF-blue" alt="Paper PDF"></a>
	<a href="NOTICE"><img src="https://img.shields.io/badge/License-Mixed_(research--only)-yellow" alt="Mixed licenses: research-only release"></a>

	[English](README.md) \| [中文](README_zh.md)

	<img src="assets/teaser.jpg" alt="OVOW teaser: monocular video to instance-level, simulation-ready 4D mesh scenes" width="92%">

	</div>

	OVOW is the first training-free system that reconstructs instance-level, simulation-ready
	4D mesh scenes from a single monocular video (or a single image): per-object 3D meshes + poses +
	deformation animation, assembled into a full-scene animated GLB with rendered comparison videos.
	Heavy models (SAM3 / Hi3DGen / FoundationPose / RoMaV2) support resident serving: loaded once
	for an entire batch.

	> ⚠️ Non-commercial research use only. OVOW's own code is MIT-licensed, but this release bundles
	> third-party components under non-commercial licenses (NVIDIA FoundationPose, CC-BY-NC SpaTrackerV2,
	> CC-BY-NC-SA Motion324, Meta SAM License sam3, etc.). By using it you agree to each component's
	> license — see [NOTICE](NOTICE) and [THIRD_PARTY.md](THIRD_PARTY.md).
	> Consequently, the assembled repository is a source-available research release, not an
	> OSI-approved open-source distribution.

	## Reproduce (from zero to results)

	Prerequisites: Linux + NVIDIA GPU (≥40GB VRAM recommended, driver ≥550) · CUDA Toolkit 12.1+
	(with `nvcc`) · conda · ~120GB disk.

	```bash
	# 0) Clone
	git clone https://github.com/SparcAI-Inc/OVOW.git && cd OVOW

	# 1) Create the 3 conda envs (installs torch + deps + compiles CUDA extensions;
	# the slowest step, ~30-60 min)
	bash environment/create_envs.sh # prints the 3 interpreter paths to paste into .env

	# 2) Machine config
	cp .env.example .env # ① paste the 3 python paths printed above
	# ② set DASHSCOPE_API_KEY, or set QWEN_BACKEND="local" (offline)

	# 3) Install blender (hard dependency of the assembly/rendering steps)
	sudo apt install -y blender # or the official portable build → set BLENDER_BIN in .env

	# 4) Localize paths (expands the repo's placeholders with your .env; one-shot —
	# to change .env later, `git checkout` the tree first, then rerun)
	bash setup.sh

	# 5) Download weights (~55GB, fully automatic; SAM3 is a gated HF repo —
	# `huggingface-cli login` and accept its terms on the web page first)
	bash download_ckpts.sh

	# 6) Reproduce the bundled examples (see below)
	bash examples/smoke_test.sh # single image, short chain, ~3-5 min
	bash examples/smoke_test_video.sh # single video, full 4D chain, ~19-22 min
	```

	If a step fails, see [Troubleshooting](#troubleshooting) below. For the per-environment
	torch/CUDA version table and the manual, step-by-step install commands that
	`create_envs.sh` automates, see [environment/README.md](environment/README.md).

	### What "reproduced" means here

	The reproduction target is the bundled `examples/` — running both of them through to
	their artifacts is the bar for a working installation. Both scripts start with a
	preflight that names exactly what is missing (Qwen backend / blender / conda
	interpreters / SAM3 and Motion324 weights) instead of failing halfway through, and both
	end by asserting that the key artifacts exist and are non-empty — a zero exit code alone
	does not count as reproduced.

	\| Script \| Input \| Steps \| Artifacts under `outputs/` \|
	\|---\|---\|---\|---\|
	\| `examples/smoke_test.sh` \| `examples/images/desk_banana.png` \| 1 2 6 7 14 17 18 (no deform) \| `smoke/image_pipeline_<ts>/desk_banana/` \|
	\| `examples/smoke_test_video.sh` \| `examples/videos/instance_000008.mp4` (768×768, 108 frames) \| full chain incl. deform \| `smoke_video/ws/instance_000008_<ts>_<key>/` \|

	Both assert these three artifacts (`.png` for the image chain, `.mp4` for the video chain):

	```
	scene_anim_blender_physics_with_deform.glb # final full-scene GLB
	fullscene_deform_objseq_white_left_rgb_right_split10_h264.{png,mp4} # white-model vs input
	fullscene_deform_objseq_textured_left_rgb_right_split10_h264.{png,mp4} # textured vs input
	```

	`smoke_test_video.sh` also takes a video path, so you can point it at your own clip:
	`bash examples/smoke_test_video.sh /path/to/your.mp4`.

	Note that the generative steps do not run with a fixed seed, so meshes and renders differ
	between runs. The bar is that the chain completes and produces the artifacts above, not
	bit-identical output.

	## Usage

	```bash
	# A single image
	bash scripts/run_image.sh --image /path/x.png --out ./outputs

	# A single video
	bash scripts/run_video.sh --name eagle --video /path/eagle.mp4 --out ./outputs

	# All videos in a folder (resident serving: models loaded once for the whole batch) — recommended at scale
	bash scripts/run_video_batch.sh --video-dir /path/videos --gpu 0 --out ./outputs

	# Mixed images+videos, multi-GPU, resumable (100k-scale)
	bash scripts/run_batch.sh --in /path/inputs --gpus 0,1,2,3 --out ./outputs
	```

	Outputs: videos under `<out>/ws/<name>_<timestamp>_<key>/`, single images under
	`<out>/image_pipeline_<timestamp>/<name>/`:
	- `scene_anim_blender_physics_with_deform.glb` — final full-scene animated GLB
	- `fullscene_deform_*_h264.mp4` — white-model / textured rendered comparison videos

	## Benchmark

	`benchmark/` holds OVOW-Bench, the evaluation toolkit used in the paper. It
	scores already-reconstructed scenes and reports Scene-IoU (AABB and OBB),
	Object-IoU after Hungarian matching with an object-count penalty, a photometric
	loss, and an N-CLIP visual metric. Geometry is compared after the alignment the
	protocol specifies; visual metrics use GT-camera renders.

	The toolkit ships evaluator code and protocol metadata only — no ground truth,
	results, or predictions. The data (`OVOW_Bench_Data`) is published separately on
	Hugging Face. It needs its own conda environment plus Blender and a local CLIP
	checkpoint, so it is deliberately kept apart from the three pipeline envs:

	```bash
	conda env create -f benchmark/environment.yml && conda activate ovow-bench
	python benchmark/scripts/check_env.py

	# unit tests need neither Blender nor CLIP weights
	cd benchmark && PYTHONDONTWRITEBYTECODE=1 python -m unittest discover -s tests
	```

	Protocol, submission layout, and the full evaluator invocation are documented in
	[benchmark/README.md](benchmark/README.md) and
	[benchmark/docs/EVALUATION_PROTOCOL.md](benchmark/docs/EVALUATION_PROTOCOL.md).

	## How it works

	<div align="center">
	<img src="assets/pipeline.png" alt="OVOW pipeline: VLM-guided scene decomposition → per-instance mesh reconstruction → scale & orientation recovery → per-frame pose tracking" width="95%">
	</div>

	\| Stage \| What it does \| Where it runs \|
	\|---\|---\|---\|
	\| Labeling \| Qwen object labeling + static/rigid/deform classification \| DashScope API or local Qwen3-VL \|
	\| Segmentation \| SAM3 per-frame masks \| sam3 env (resident sam3-server in batch mode) \|
	\| Deformable meshes \| Motion324 + Hunyuan \| motion env \|
	\| Static/rigid reconstruction \| Hi3DGen meshes + FoundationPose poses + RoMaV2 scale recovery \| opg env (resident opg-server in batch mode) \|
	\| Deformable poses \| frame_track per-frame poses \| opg env (same as above) \|
	\| Assembly / rendering \| Full-scene GLB export + rendered comparisons \| blender \|

	### Pipeline steps

	The entry scripts accept an explicit list of step numbers as trailing arguments
	(`bash scripts/run_video.sh --name eagle --video x.mp4 1 2 3 4 5 6 7 8 10 13 14 17 18`).
	With no steps given, `run_video.sh` runs the full chain and `run_image.sh` runs the
	image-only subset. Steps 15/16/19 are optional and off by default. Steps 15 and 19
	additionally need components this repository does not ship and you fetch yourself,
	one of them under academic-use-only terms — see the optional-components table in
	[THIRD_PARTY.md](THIRD_PARTY.md) before enabling them.

	\| step \| What it does \| env / service \|
	\|---\|---\|---\|
	\| 1 \| Qwen object labeling + static/rigid/deform classification \| API or local Qwen3-VL \|
	\| 2 \| SAM3 per-frame masks \| sam3 env (sam3-server in batch mode) \|
	\| 3 \| Build deform inputs (object-centered video) \| opg (light) \|
	\| 4 \| Deformable mesh generation \| Motion324 (motion env) \|
	\| 5 \| Scan Motion324 outputs → manifest \| light \|
	\| 6 \| Static/rigid reconstruction (mesh + pose) \| opg-server (resident) \|
	\| 7 \| Ground-plane fitting \| opg (geometry) \|
	\| 8 \| Deformable poses (frame_track) \| opg-server (resident) \|
	\| 10 \| Merge full-scene mesh sequence \| blender \|
	\| 13 / 14 \| Export full-scene GLB (pre-physics / physics) \| blender \|
	\| 17 / 18 \| Render white-model / textured comparison videos \| blender \|

	Default reconstruction backbone is `uplegacy` = legacy/Hi3DGen generation plus the
	upgraded pose chain (romav2 matching + zero-iteration scale recovery + all models
	resident in-process). It needs RoMaV2 and the Hi3DGen / SpaTrackerV2 / FoundationPose
	weights (all covered by step 5 above) and does not need TRELLIS2/CuMesh — those are
	only for `--recon-backbone trellis2`.

	Runtime is ~19–22 min per video end-to-end, almost entirely compute (step 6 ~6 min +
	deform ~9–11 min + rendering); model loading is a one-off ~1–3 min service startup.

	### Resident serving (batch mode)

	`run_video_batch.sh` and `run_batch.sh` start and stop opg-server + sam3-server
	automatically, each with its own queue — nothing to configure. Heavy models
	(SAM3, opg core, RoMaV2, FoundationPose) are then loaded once for the whole batch
	instead of once per video; Motion324 + Hunyuan (step 4) still load per video.
	Resident serving pays off across many videos — for a single video it is a wash,
	so single runs (`run_image.sh` / `run_video.sh`) stay in-process by default.

	To manage the services manually across several batches:

	```bash
	bash scripts/servers/opg_server.sh ensure --gpu 0 # ensure = start only if not already running
	bash scripts/servers/opg_server.sh status
	bash scripts/servers/opg_server.sh stop
	bash scripts/servers/sam3_server.sh ensure --gpu 0
	```

	To make a single run reuse manually started services, export the queues explicitly.
	The engine treats "queue variable non-empty" as "use the server" and does not
	health-check it, so exporting a queue with no server running will hang until timeout:

	```bash
	export OPG_SERVER_QUEUE=/tmp/opg_server_main_q SAM3_SERVER_QUEUE=/tmp/sam3_server_q
	bash scripts/run_video.sh --name eagle --video /path/eagle.mp4
	```

	### Key environment variables

	All have sensible defaults in [configs/paths.sh](configs/paths.sh); override in `.env`.

	\| Variable \| Effect \|
	\|---\|---\|
	\| `OPG_SERVER_QUEUE` / `SAM3_SERVER_QUEUE` \| route step 6/8 and step 2 through the resident services \|
	\| `OPG_MATCHER=romav2` \| use RoMaV2 dense matching for scale recovery \|
	\| `OPG_SCALE_REFINE_ITERS=0` \| zero-iteration scale recovery (~5x faster; converges on the first pass under romav2) \|
	\| `OPG_ROMAV2_INPROC=1` \| keep RoMaV2 resident in-process \|
	\| `OPG_FP_RESIDENT=1` \| keep the FoundationPose predictor as an in-process singleton \|
	\| `QWEN_BACKEND=local` \| run step 1 labeling on a local Qwen3-VL instead of the DashScope API \|
	\| `HF_ENDPOINT=https://hf-mirror.com` \| mirror for weight downloads \|

	## Troubleshooting

	Environment build (step 1) — the most likely thing to need per-platform tuning.
	The `opg` env compiles extensions on the machine (flash-attn / pytorch3d /
	FoundationPose). `create_envs.sh` handles each env independently and can be re-run
	per env (`bash environment/create_envs.sh opg`); the version table and the equivalent
	manual commands are in [environment/README.md](environment/README.md).

	- `nvcc not found` → install CUDA Toolkit 12.1+ and put it on `PATH`.
	- arch/ABI mismatch → confirm torch is the cu121 wheel and `nvcc -V` is ≥ 12.1.
	- flash-attn compiles too slowly or fails → use an official prebuilt wheel matching cu121 + torch 2.4.

	No DashScope account (step 2). Set `QWEN_BACKEND="local"` in `.env` to run step 1
	on a local Qwen3-VL. The image path pulls `Qwen/Qwen3-VL-8B-Instruct` from HF
	automatically; you can also predownload with
	`masking/download_qwen3_vl_8b_hf_mirror.sh` and point at it with `--qwen-model /path`.

	Placeholder expansion is one-shot (step 4). `setup.sh` runs `configure.sh`, which
	expands `@ENGINE_DIR@`, `@OPG_PY@`, `@BLENDER_BIN@` and friends into the paths from
	your `.env`. Once expanded the tokens are consumed, so re-running it after editing
	`.env` will not update them. To reconfigure, restore the tree first:

	```bash
	git checkout -- engine/ third_party/ masking/ && bash configure.sh
	```

	Weights (step 5).
	- `facebook/sam3` is a gated HF repo: `huggingface-cli login` and accept its terms on the model page first.
	- RoMaV2 weights need no download — torch.hub fetches them on first run.
	- TRELLIS weights are only needed for `--recon-backbone trellis2`: `OVOW_DL_TRELLIS2=1 bash download_ckpts.sh` (+~20GB).
	- The script creates two symlinks the engine expects: `engine/checkpoints/OnePoseViaGen → ckpts/OnePoseViaGen` and `third_party/sam3/checkpoints/sam3/sam3.pt → ckpts/sam3/sam3.pt`.

	Runtime errors.
	- `server not ready` → the service is still loading (opg takes ~1–3 min); check the service log in the queue directory.
	- `motion manifest missing` (step 8) → step 5 did not run; make sure your step list includes `5`.
	- `API key empty` → check that `APIKEY_FILE` exists and contains `sk-...`, or switch to offline labeling with `QWEN_BACKEND=local`.
	- Services will not stop → `bash scripts/servers/opg_server.sh stop`, or `pkill -f opg_server.py`.

	## Citation

	If you find OVOW useful for your research, please cite:

	```bibtex
	@inproceedings{chen2026ovow,
	title = {One Video, One World: Turning Monocular Video into Physical 4D Scenes},
	author = {Chen, Junhao and Zhang, Boran and Chen, Mingjin and Zhang, Henghaofan and
	Zhang, Saining and Zhu, Congcong and Zhao, Hao and Huang, Ruqi and
	Li, Zhihao and Wang, Yufei},
	booktitle = {European Conference on Computer Vision (ECCV)},
	year = {2026}
	}
	```

	## Acknowledgements

	OVOW is an orchestration framework built on top of many excellent publicly released research works.
	We sincerely thank the authors of:

	Core engine
	- [OnePoseviaGen](https://github.com/GZWSAMA/OnePoseviaGen) (One View, Many Worlds) — the
	reconstruction engine of this project; OVOW's name is also a tribute to it.

	Works integrated by the engine / depended on by this project
	- [FoundationPose](https://github.com/NVlabs/FoundationPose) — 6-DoF object pose estimation & tracking (NVIDIA)
	- [SpaTrackerV2 / SpatialTrackerV2](https://github.com/henry123-boy/SpaTrackerV2) — 3D point tracking
	- [Hunyuan3D-2](https://huggingface.co/Tencent-Hunyuan/Hunyuan3D-2) — Hi3DGen 3D generation (Tencent Hunyuan)
	- [TRELLIS](https://github.com/microsoft/TRELLIS) — 3D generative representation (Microsoft)
	- [Depth-Anything-3 (DA3)](https://github.com/DepthAnything) — monocular depth
	- [SAM 2](https://github.com/facebookresearch/sam2) / [SAM 3](https://github.com/facebookresearch/sam3) — segmentation (Meta)
	- [Motion324](https://github.com/Inception3D/Motion324) — deformable mesh animation
	- [RoMaV2](https://github.com/Parskatt/RoMaV2) — dense feature matching (scale recovery / PnP correction)
	- [Amodal3R](https://github.com/Sm0kyWu/Amodal3R) — occlusion-aware 3D reconstruction
	- [BundleSDF](https://github.com/NVlabs/BundleSDF) — pose refinement (NVIDIA)
	- [UniDepth](https://github.com/lpiccinelli-eth/UniDepth) — metric depth estimation (ETH)
	- [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL) — multimodal labeling (Alibaba)

	Per-component licenses and provenance: [THIRD_PARTY.md](THIRD_PARTY.md).

	## License

	OVOW-authored code is released under the MIT license (see [LICENSE](LICENSE)).
	However, third-party components bundled in this release carry non-commercial licenses,
	so the assembled repository uses mixed licenses and is for
	non-commercial research / evaluation use only —
	see [NOTICE](NOTICE) and [THIRD_PARTY.md](THIRD_PARTY.md).