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Aria Gen 1/2 toolkit: MANO hand fitting, stereo depth, and real-time streaming.

aria2mano 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# aria2mano

Aria [Gen 1](https://facebookresearch.github.io/projectaria_tools/docs/intro) / [Gen 2](https://facebookresearch.github.io/projectaria_tools/gen2/) toolkit with two workflows:

1) [**Offline Processing (Gen 1/2)**](#offline-processing-gen-12) - VRS recordings → ML-ready data with anatomically correct [MANO](https://mano.is.tue.mpg.de/) hands.
2) [**Real-time Streaming (Gen 2)**](#real-time-streaming-gen-2) - Live stereo depth (up to 30 FPS) + MANO fitting over USB or untethered WiFi.

## Installation

Installation tested on Ubuntu 22.04 LTS / 24.04 LTS with CUDA 12.8, PyTorch 2.9.1, and Python 3.10.

```bash
# 1) Clone and install dependencies
git clone git@github.com:KevinyWu/aria2mano.git && cd aria2mano
conda env create -f environment.yaml && conda activate aria2mano
pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cu128
pip install --no-build-isolation git+https://github.com/mattloper/chumpy.git@580566e

# 2) Install aria2mano
pip install -e .

# 3) Download required assets listed below
```

<details><summary>📦 Requirements</summary>

**MANO Models**

- [Register](https://mano.is.tue.mpg.de/) → download and unzip the MANO models → copy contents of `mano_v*_*/` to `assets/mano_models/`
- ***Required*** for MANO hand mesh fitting

**S2M2 Models**

- Download weights from [S2M2 repository](https://github.com/junhong-3dv/s2m2) → copy all four model weights to `assets/s2m2/`
- ***Required*** for depth estimation

**Aria Devignetting Masks**

- Download from [Aria Gen 1 Docs](https://facebookresearch.github.io/projectaria_tools/docs/data_utilities/advanced_code_snippets/image_utilities#image-devignetting) → unzip and copy `new_isp` and `old_isp` to `assets/aria_devignetting_masks/`
- ***Optional*** for Gen 1 image enhancement

</details>

## Offline Processing (Gen 1/2)

<img src="assets/img/aria2mano.gif" alt="aria2mano" width="100%"/>
 
 Process Aria recordings with MANO hand fitting and metric stereo depth.

- ***MANO hand fitting*** - Anatomically correct hand meshes from Aria hand tracking landmarks.
- ***Stereo depth*** - Dense depth maps via [S2M2](https://junhong-3dv.github.io/s2m2-project/) (Gen 2 only).
  - > S2M2 XL (30 FPS w/ TensorRT) outperforms FoundationStereo (2 FPS; similar quality) and Fast-FoundationStereo (45 FPS w/ TensorRT; lower quality).
- ***Easy data access*** - `AriaLoader` class for simple frame-by-frame data retrieval.
- ***Web visualization*** - Interactive 3D viewer with [Viser](https://viser.studio/main/).

### Quick Start

After downloading VRS recordings to your local machine:

```bash
# 1) Sort VRS files
./scripts/sort_vrs.sh "<path/to/all/vrs/files>"

# 2) Run MPS
aria_mps single -i "<path/to/all/vrs/files>"

# 3) Process Aria recordings (~3.8GB/job, set `--max-jobs-per-gpu` accordingly)
./scripts/process_aria.sh "<path/to/all/vrs/files>" --max-jobs-per-gpu 1

# 4) Viser visualization
python -m aria2mano.viser_viewer --aria-path "<path/to/aria/recording/folder>"
```

### Detailed Usage

<details><summary>🗂️ 1) Sort VRS Files</summary>

Organize recordings and run health checks. For Gen 2, also extracts on-device data.

```bash
./scripts/sort_vrs.sh "<path/to/all/vrs/files>" [OPTIONS]

# See all options
./scripts/sort_vrs.sh --help
```

> **Note**: As of 03/16/2026, a firmware bug causes sporadic health check failures. The script trims the first second of affected recordings by default (turn off with `--no-trim`).

</details>

Run Aria's [Machine Perception Services](https://facebookresearch.github.io/projectaria_tools/gen2/technical-specs/mps/mps_cli_guide) for cloud-based SLAM and hand tracking.

```bash
aria_mps single -i "<path/to/all/vrs/files>"
```

For Gen 2 recordings, running MPS is optional. Use `--on-device` flag in step 3 to process with on-device data instead. However, **MPS data is more accurate**. See [Troubleshooting MPS Errors](#appendix) for common error codes.

> **⚠️ MPS Warning**: SLAM poses and hand tracking are not accurate for the first ~1-2 seconds of the recording.

</details>

<details><summary>⚙️ 3) Process Aria Recordings</summary>

Recordings with existing outputs are skipped by default. Pass `--reprocess` to overwrite.

**Batch processing**

```bash
./scripts/process_aria.sh "<path/to/all/vrs/files>" --max-jobs-per-gpu 1 [OPTIONS]

# See all options
./scripts/process_aria.sh --help
```

> **⚠️ GPU Memory Warning**: Each job uses ~3.8GB VRAM with PyTorch or ~2.0GB with TensorRT. Set `--max-jobs-per-gpu N` (default: 1) based on your GPU capacity.

**Single recording**

```bash
# Full processing (default)
python -m aria2mano.aria_processor --aria-path "<path/to/aria/recording/folder>"

# Fast mode (no MANO optimization or depth)
python -m aria2mano.aria_processor --aria-path "<path/to/aria/recording/folder>" --no-optimize-mano --no-get-depth
```

Each run prints a performance summary table. See [Benchmarking](#appendix) for details.

**Common overrides**

Both batch and single recording commands accept the same processing flags.

| Flag | Effect |
| ---- | ------ |
| `--reprocess` | Overwrite existing outputs (skipped by default) |
| `--no-optimize-shape` | Skip per-frame shape optimization ([MANO Optimization](#appendix)) |
| `--frozen-shape` | Use a pre-defined MANO shape ([MANO Optimization](#appendix)) |
| `--tensorrt` | Use the TensorRT depth engine instead of PyTorch; off by default |
| `--on-device` | Gen 2 only: force processing to use on-device data |
| `--downsample-rgb N` | Shrink saved RGB JPEGs; stored `rgb_camera.K`/`width`/`height` scale accordingly |

**Summary CSV**

This might be slow for large datasets, pass `--simple` to get a quick summary.

```bash
./scripts/summarize_data.sh "<path/to/all/vrs/files>"
```

> **Note**: Wrist 6DoF (`T_device_wrist`) comes from Aria, not MANO; MANO only solves finger `pose` and `shape`. See [Hand Tracking Conventions](#appendix) for details.

</details>

<details><summary>🎨 4) Viser Visualization</summary>

Launch the [Viser](https://viser.studio/main/) web viewer. The floor plane is detected with RANSAC on the MPS semidense point cloud.

```bash
python -m aria2mano.viser_viewer --aria-path "<path/to/aria/recording/folder>"

# See all options
python -m aria2mano.viser_viewer --help
```

Open `http://localhost:8080` in your browser.

> **Tip**: For long recordings, you may want to set `--max-frames`, `--start-frame` and `--end-frame` (both support negative indices), or apply downsampling to the depth point cloud and images with `--downsample`.
>
> **⚠️ MPS Warning**: SLAM poses and hand tracking are not accurate for the first ~1-2 seconds of the recording, hence `--start-frame` defaults to 20.

</details>

<details><summary>💿 Aria Data Loader</summary>

Access processed data programmatically:

```python
from aria2mano.aria_loader import AriaLoader

loader = AriaLoader(aria_path="path/to/aria/recording/folder")

# Metadata
num_frames = loader.get_num_frames()
fps = loader.get_fps()

# Per-frame data (see aria_data_classes.py for structure)
frame = loader.get_frame_data(index=0)
rgb = loader.get_rgb_image(index=0)
point_cloud = loader.get_point_cloud()

# Gen 2 only
stereo_left = loader.get_stereo_left_image(index=0)
stereo_right = loader.get_stereo_right_image(index=0)
depth = loader.get_depth_image(index=0)
depth_mask = loader.get_depth_mask_image(index=0)
```

</details>

<details><summary>📁 Data Format</summary>

After processing, each recording has this structure. See [`aria_data_classes.py`](src/utils/aria_data_classes.py) for the contents of the `aria_data.pkl` file, [Data Format Details](#appendix) for coordinate conventions, and [Hand Tracking Conventions](#appendix) for landmark indexing.

```text
recording_name/
├── video.vrs                    # Original Aria recording
├── vrs_health_check.json
├── mps_video_vrs/               # Cloud MPS results
│   ├── hand_tracking/
│   │   ├── hand_tracking_results.csv
│   │   └── summary.json
│   └── slam/
│       ├── closed_loop_trajectory.csv
│       ├── open_loop_trajectory.csv
│       ├── semidense_observations.csv.gz
│       ├── semidense_points.csv.gz
│       ├── online_calibration.jsonl
│       └── summary.json
├── on_device/                   # Gen 2 only: on-device results
│   ├── eye_gaze/
│   ├── hand_tracking/
│   └── slam/
└── processed/                   # Output from aria_processor.py
    ├── aria_data.pkl            # Metadata + per-frame hand tracking
    ├── point_cloud.npz          # World-frame 3D points (Nx3)
    ├── rgb/{:08d}.jpg           # Undistorted RGB images
    ├── stereo_left/{:08d}.png   # Gen 2 only: scanline-rectified stereo left
    ├── stereo_right/{:08d}.png  # Gen 2 only: scanline-rectified stereo right
    ├── depth/{:08d}.png         # Gen 2 only: depth maps (uint16, 1mm units)
    └── depth_mask/{:08d}.png    # Gen 2 only: depth validity mask (255=valid)
```

</details>

<details><summary>⚡ TensorRT Acceleration</summary>

We can speed up stereo depth estimation with [TensorRT](https://developer.nvidia.com/tensorrt), NVIDIA's inference optimizer. Engines are tied to the GPU architecture and TensorRT version they were built on. For best performance, build the engine on the same GPU that will run inference.

**Build the engine**

See the [TensorRT docs](https://docs.nvidia.com/deeplearning/tensorrt/latest/installing-tensorrt/installing.html) for installation instructions. We use CUDA 12.8 and install with pip, hence `tensorrt-cu12`.

```bash
# Install dependencies
pip install onnxruntime-gpu onnxscript tensorrt-cu12

# Build the engine
python -m aria2mano.build_tensorrt
```

This exports S2M2 to ONNX at `assets/s2m2/onnx/`, then builds a TensorRT engine at `assets/s2m2/trt/`.

**Using the TensorRT engine**

Enable/disable TensorRT in [`config.yaml`](src/config.yaml), where you can also specify engine parameters. Alternatively, override the config per run with `--tensorrt` or `--no-tensorrt`.

```bash
# Batch offline processing
./scripts/process_aria.sh "<path/to/all/vrs/files>" --tensorrt

# Single recording / streaming subscriber
python -m aria2mano.aria_processor --aria-path "<path/to/aria/recording/folder>" --tensorrt
python -m aria2mano.stream_sub --pub-host "<publisher-ip>" --tensorrt
```

**Inference speed comparison on NVIDIA RTX 5090**

`model_type: XL`, `num_refine: 3`, `img_width: 512`, `img_height: 512`, `precision: fp16`

| Backend | FPS | Memory |
| ------- | --- | ------ |
| PyTorch | ~12 | 4744M (3716M with expandable segments \*) |
| TensorRT | ~30 | 1994M |

> \* Enable with `export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`.

</details>

## Real-time Streaming (Gen 2)

<img src="assets/img/aria2mano_live.gif" alt="aria2mano_live" width="100%"/>
 
 30 FPS stereo depth and MANO fitting, streaming wirelessly over WiFi.

- ***Wireless streaming with pub/sub*** - Start streaming on a laptop from anywhere; [commlink](https://github.com/jeffacce/commlink) TCP streams to a powerful remote machine for stereo depth/MANO fitting.
- ***Subscriber can run anywhere*** - Only the publisher machine needs Aria Client SDK; the subscriber runs on any platform even if Client SDK is unsupported (e.g. ARM devices: Jetson, Raspberry Pi, etc).
- ***Teleop mode*** - Streams only hands + VIO at native 30 Hz for low-thermal, long-duration sessions.

### Quick Start

For WiFi streaming (mode B), connect the glasses to the same WiFi network as the publisher machine, then:

```bash
# 1) On the publisher (glasses plugged in via USB)
aria_gen2 streaming install-certs # For the first stream on this device
python -m aria2mano.stream_pub --wifi

# 2) Unplug the glasses (streaming may pause for a few seconds)

# 3) On the subscriber (omit --pub-host if on the same machine)
python -m aria2mano.stream_sub --pub-host "<publisher-ip>"

# 4) Stop streaming: press the action button on the glasses
#    Ctrl+C doesn't stop streaming in WiFi mode
```

> [!TIP]
> For best real-time depth performance (~30 FPS inference), build a TensorRT engine with S2M2 XL on the subscriber machine, then enable it per run with `--tensorrt`.

### Detailed Usage

<details><summary>📨 Pub/Sub Streaming</summary>

Streaming is split into a publisher and subscriber that talk over [commlink](https://github.com/jeffacce/commlink), an RPC layer built on ZeroMQ. We use custom streaming profiles with reduced RGB resolution (2560x1920 → 640x480) to ensure stable streaming without thermal issues. See [Custom Streaming Profiles](#appendix) for details.

***Two main motivations for this setup:***

1) Start streaming from anywhere with a laptop; run stereo depth and MANO fitting on a remote machine with a GPU.
2) The publisher owns all `aria` and `projectaria_tools` imports and must run on a [Aria Client SDK supported device](https://facebookresearch.github.io/projectaria_tools/gen2/ark/client-sdk/start#supported-platforms); the subscriber has no Aria dependencies and runs anywhere even if Client SDK is unsupported (e.g. ARM devices: Jetson, Raspberry Pi).

![aria_streaming](https://raw.githubusercontent.com/KevinyWu/aria2mano/main/assets/img/aria_streaming.png)

There are two links to set up:

1) ***Glasses ↔ Publisher*** - USB (default) or WiFi (`--wifi`, glasses and publisher must be on the same WiFi network).
2) ***Publisher ↔ Subscriber*** - TCP on `pub_port` (default `5555`); pass the publisher's reachable IP to the subscriber (LAN, [Tailscale](https://tailscale.com/), `localhost` if on the same machine, etc.).

Open `http://localhost:8080` from the subscriber machine to view. The publisher sends an initial `meta` handshake so the subscriber auto-configures its Viser GUI to match the active streams.

```bash
# See all options
python -m aria2mano.stream_pub --help
python -m aria2mano.stream_sub --help
```

</details>

Plug the glasses into the publisher machine via USB. The subscriber can either be on the same machine as the publisher or a remote machine reachable over TCP (LAN, [Tailscale](https://tailscale.com/), etc.).

```bash
# 1) On the publisher (glasses plugged in via USB)
python -m aria2mano.stream_pub

# 2) On the subscriber (omit --pub-host if on the same machine)
python -m aria2mano.stream_sub --pub-host "<publisher-ip>"

# 3) Ctrl+C on the publisher to stop streaming
```

> **Quality Tips**: When pub/sub are on the same machine, streaming quality can be increased:
>
> - Raise image frequency in [`streaming_profile.json`](assets/streaming_profile.json) (i.e. 10 Hz → 15 Hz, **must bump SLAM/RGB cameras together**).
> - Raise RGB image size in [`streaming_profile.json`](assets/streaming_profile.json) (i.e. 640x480 → 2560x1920, must bump `rgbCamera.resolution` to `R_4032x3024` and preserve 4:3 aspect ratio).
> - Raise `--jpeg-quality` on the publisher (i.e. 80 → 90).
> - Use the built-in streaming [profile9](https://facebookresearch.github.io/projectaria_tools/gen2/technical-specs/device/profile) via `python -m aria2mano.stream_pub --profile-name profile9`.
> - Quality has tradeoffs. E.g. increase image size but reduce the frequency 10 Hz → 5 Hz.

</details>

Use WiFi when you want the glasses untethered. The glasses and the publisher machine must be on the same WiFi network - see [WiFi router streaming](https://facebookresearch.github.io/projectaria_tools/gen2/ark/client-sdk/streaming#wifi-router-streaming).

> **⚠️ Streaming Warning**: If the glasses overheat on WiFi, bump `--batch-period-ms` (e.g. `python -m aria2mano.stream_pub --wifi --batch-period-ms 200`).

```bash
# 1) On the publisher (glasses plugged in via USB)
python -m aria2mano.stream_pub --wifi

# 2) Unplug the glasses (streaming may pause for a few seconds)

# 3) On the subscriber (omit --pub-host if on the same machine)
python -m aria2mano.stream_sub --pub-host "<publisher-ip>"

# 4) Stop streaming: press the action button on the glasses
#    Ctrl+C doesn't stop streaming in WiFi mode
```

> **Latency Tips**: To reduce end-to-end glasses → viewer delay:
>
> - Run `stream_pub` and `stream_sub` on the same machine (skips the network hop).
> - Lower image frequency in [`streaming_profile.json`](assets/streaming_profile.json) (i.e. 10 Hz → 5 Hz).
> - Lower RGB image size in [`streaming_profile.json`](assets/streaming_profile.json) (i.e. 640x480 → 320x240, must preserve 4:3 aspect ratio).
> - Lower `--jpeg-quality` on the publisher (i.e. 80 → 65).

</details>

<details><summary>🤖 Mode C - Teleop</summary>

30 Hz hands + VIO only using [`teleop_profile.json`](assets/teleop_profile.json), designed with robot teleoperation in mind. Combined with subscriber `--no-optimize-mano`, this is the lightest path, which avoids the thermal load of long image streams. Both USB and WiFi modes are supported, WiFi example below.

```bash
# 1) On the publisher (glasses plugged in via USB)
python -m aria2mano.stream_pub --hands-only --wifi

# 2) Unplug the glasses (streaming may pause for a few seconds)

# 3) On the subscriber (omit --pub-host if on the same machine)
python -m aria2mano.stream_sub --pub-host "<publisher-ip>" --no-optimize-mano

# 4) Stop streaming: press the action button on the glasses
#    Ctrl+C doesn't stop streaming in WiFi mode
```

</details>

## Appendix

<details><summary>📐 Data Format Details</summary>

- ***Transforms***: Use `T_destination_source` notation. All hand landmarks are in [device frame](https://facebookresearch.github.io/projectaria_tools/gen2/technical-specs/coordinate/cpf-coor#nuance-1-coordinate-frames-as-se3-are-actually-poses-from-an-arbitrary-reference-device-frame).
- ***Camera frames***: OpenCV convention (+X right, +Y down, +Z forward), origin at optical center. See [Aria camera coordinate system](https://facebookresearch.github.io/projectaria_tools/gen2/technical-specs/coordinate/3d-coor#camera-coordinate-system-convention).
- ***`--on-device` mode***: `T_world_device = T_odometry_device` (from on-device VIO, Gen 2 only).
- ***RGB images***: Undistorted to linear camera model.
- ***Stereo images***: Scanline-rectified to linear camera model (Gen 2 only).
- ***Depth maps***: `uint16` format, 1 unit = 1mm, in stereo left camera frame (Gen 2 only).
- ***Depth masks***: `uint8` format, 255=valid, 0=invalid (Gen 2 only).

</details>

<details><summary>🖐️ Hand Tracking Conventions</summary>

The canonical Aria wrist frame and the canonical MANO wrist frame coincide.

| Direction | Left Aria/MANO | Right Aria/MANO |
| --- | --- | --- |
| Wrist → middle | +X | -X |
| Palm normal | -Y | -Y |
| Wrist → thumb | +Z | +Z |

> **Note**: The image below shows the left hand. For the right hand, the wrist frame's Z and X axes are flipped, i.e. rotated 180 degrees around the Y axis. The +Z axis points towards the thumb on both hands.

Aria and MANO use different indexing for their 21 hand landmarks:

- **Aria**: Has palm center (idx 20), no thumb CMC
- **MANO**: Has thumb CMC (idx 1), no palm center

<table align="center">
 <tr>
 <td width="50%" align="center"><img src="assets/img/aria_hand.png" alt="Aria hand landmarks" width="100%"/></td>
 <td width="50%" align="center"><img src="assets/img/mano_hand.png" alt="MANO hand landmarks" width="100%"/></td>
 </tr>
 <tr>
 <td align="center">Aria Hand Landmarks</td>
 <td align="center">MANO Hand Landmarks</td>
 </tr>
</table>

In `aria_data.pkl`, landmarks are stored in their original conventions. MANO optimization ignores thumb CMC and palm landmarks, using the 19 shared landmarks. See [`mano_hand_utils.py`](src/utils/mano_hand_utils.py) for conversion utilities.

</details>

<details><summary>🔧 MANO Optimization</summary>

MANO optimization code is located in [`mano_optimizer.py`](src/utils/mano_optimizer.py) and uses the MANO implementation from [manotorch](https://github.com/lixiny/manotorch). Note that wrist 6DoF (`T_device_wrist`) is taken from Aria hand tracking, not MANO. Landmarks are transformed into the wrist frame before fitting, so MANO only solves the 15 finger joint rotations (`pose`) and shape (`shape`) relative to a wrist at the origin.

> **Tip**: For temporal data, initializing the `ManoOptimizer.optimize_mano` with the previous frame's pose and shape (`init_pose` and `init_shape`) vastly improves optimization speed and quality.

Two CLI flags control shape behavior:

| Flag | Default | Effect |
| ---- | ------- | ------ |
| `optimize_shape` | `true` | Optimize betas per frame. If `false`, betas are initialized from the previous frame (only optimized at first frame). |
| `frozen_shape` | `false` | Load betas from `assets/mano_rhand_shape.npy` and freeze them. Overrides `optimize_shape`. |

- **`optimize_shape: false`** - Useful when you only care about pose fitting and want faster optimization or want to keep the shape fixed.
- **`frozen_shape: true`** - Useful when you have a calibrated **right hand** shape and want that exact shape used consistently across all frames/recordings. Save a NumPy array of 10 MANO **right hand** beta coefficients to `assets/mano_rhand_shape.npy` (accepted formats: 1D `(10,)` or 2D `(1, 10)`). The left hand will use the same shape.

</details>

<details><summary>🎛️ Custom Streaming Profiles</summary>

The publisher picks a profile JSON as shown in the *Select with* row below. You can also pass your own profile JSON with `--profile-path`.

| Stream | Key | [`streaming`](assets/streaming_profile.json) | [`teleop`](assets/teleop_profile.json) | [`profile9`](https://facebookresearch.github.io/projectaria_tools/gen2/technical-specs/device/profile) |
| ------ | --- | ----------- | ------------ | ------------ |
| ***Select with*** | -- | default | `--hands-only` | `--profile-name profile9` |
| Stereo SLAM (L+R) | `slamCameras` | 10 Hz, 512x512 | -- | 10 Hz, 512x512 |
| RGB camera | `rgbCamera` | 10 Hz, 640x480 | -- | 5 Hz, 2560x1920 |
| Hand tracking | `ht` | 30 Hz | 30 Hz | 30 Hz |
| VIO (for online calibration) | `vio` | 10 Hz | 10 Hz | 10 Hz |
| High-frequency pose | `vio_high_frequency_pose` | 800 Hz | 800 Hz | -- |

RGB resolution is controlled by `rgbCamera.resolution` (camera sensor mode, e.g. `R_2016x1512`) plus the `width`/`height` fields (downscale target). SLAM cameras stream at their native resolution of 512x512. High-freq pose rate is reduced by `--vio-skip 10` (default every 10th sample → ~80 Hz); pass `--vio-skip 1` to disable.

> **Note**: Any custom `width`/`height` must preserve the sensor's native 4:3 aspect ratio (e.g. 2560x1920, 2016x1512, 640x480, 320x240). Non-4:3 values will not result in linear rectification.

</details>

<details><summary>📊 Benchmarking</summary>

MANO optimization (CPU) and depth estimation (GPU) performance highly depends on your hardware. Depth estimation can be sped up with [TensorRT](#detailed-usage) while MANO optimization trades off speed and accuracy by adjusting optimization parameters in [`config.yaml`](src/config.yaml).

The following benchmarks are performed on a AMD Ryzen Threadripper PRO 7975WX 32-Cores CPU and NVIDIA RTX 5090 GPU.

**Offline processing**

Batch processing allocates multiple `aria2mano.aria_processor` jobs on the machine, so per-recording performance will be affected by the number of jobs. We suggest processing a single recording with `aria2mano.aria_processor` and checking the performance table terminal output. Here is an example on a 20 second recording (200 frames).

| Stage | FPS | ms/frame |
| --------------------------- | -----: | -------: |
| MANO optimization (CPU) | 27.2 | 36.8 |
| Stereo rectification (CPU) | 103.4 | 9.7 |
| Depth estimation (GPU, PyTorch) | 12.1 | 82.9 |
| Depth estimation (GPU, TensorRT) | 30.9 | 32.3 |

**Real-time streaming**

Real-time streaming performance depends on both hardware and the network. In this setup, Aria glasses stream to an M4 Macbook Pro over WiFi, which publishes to the aforementioned machine located **3 miles away**. This table is a snapshot of the streaming performance with TensorRT enabled for depth estimation.

| Stream | FPS | p50 latency (ms) | p95 latency (ms) | Bandwidth |
| ------------ | ---: | -----: | -----: | ---------: |
| Online calibration | 2.0 | 96.3 | 225.2 | 12.8 kbps |
| High-frequency VIO | 57.2 | 30.6 | 44.2 | 123.1 kbps |
| Hand tracking | 29.2 | 35.9 | 53.3 | 478.6 kbps |
| Images (RGB + stereo left/right) | 10.0 | 52.5 | 65.5 | 7.09 Mbps |
| MANO optimization (CPU) | 37.5 | -- | -- | -- |
| Depth estimation (GPU, TensorRT) | 30.1 | -- | -- | -- |

</details>

<details><summary>🚨 Troubleshooting MPS Errors</summary>

See all [MPS error codes](https://facebookresearch.github.io/projectaria_tools/gen2/ark/support/mps#error-codes).

| Code | Description | Action |
| ------ | ------------- | -------- |
| 102 | Health check failed | Discard recording |
| 108 | Failed to query server | Retry |
| 115 | Error checking processing status | Retry |
| 50XX | Internal server error | Force re-upload: `aria_mps single -i "<path>" -s a --force` |

</details>

## Citation

If you use aria2mano, please cite our paper [Human Universal Grasping](https://grasping.io/), which aria2mano is originally developed for:

```bibtex
@article{wu2026hug,
    title={Human Universal Grasping},
    author={Kevin Yuanbo Wu and Tianxing Zhou and Isaac Tu and Billy Yan and Irmak Guzey and David Fouhey and Dandan Shan and Lerrel Pinto},
    journal={arXiv preprint arXiv:2606.17054},
    year={2026}
}
```

Additionally, we thank the authors of [MANO](https://mano.is.tue.mpg.de/), [manotorch](https://github.com/lixiny/manotorch), and [S2M2](https://github.com/junhong-3dv/s2m2) for their excellent work.

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