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e36d0b4

---
license: apache-2.0
pipeline_tag: image-to-3d
tags:
- pear
- human-mesh-recovery
- smplx
- robotics
- IB-Robot
- ascend
- edge-deployment
base_model:
- BestWJH/PEAR_models
---

# Model Card for PEAR Parameter Network (IB-Robot)

The parameter-regression network of PEAR (Pixel-aligned Expressive humAn mesh Recovery),
packaged for the [IB-Robot](https://atomgit.com/openeuler/IB_Robot) framework. From a
person crop it regresses SMPL-X / FLAME / camera parameters for expressive 3D human
understanding in HRI.

Person crops come from
[openEuler/yolox_x_640](https://huggingface.co/openEuler/yolox_x_640).

**Scope:** this bundle contains the PEAR image encoder and parameter regression heads only.
EHM / SMPL-X LBS, mesh generation and rendering stay on the host CPU and are **not** part of
the OM.

## Repository Structure

- `inference_manifest.json` — deployment routing (schema v3)
- `assets/adapter.json` — adapter identity (`pear_parameter_network` / `predict_parameters`)
- `assets/pear_model.pt` — original PyTorch checkpoint the OM was converted from
- `artifacts/ascend_310p/pear_parameter_network_bs1.om` — Ascend 310P1 OM (batch 1)

## Deployment Backends

| Target | Backend | Runtime | Hardware |
|--------|---------|---------|----------|
| `ascend_310p` | ascend | ACL | Ascend 310P1 |

**Input:** `pear.input` float32 [1,3,256,256] NCHW (`input`)

**Outputs:** eight tensors, fixed order

| # | semantic | shape |
|---|----------|-------|
| 0 | `smplx_pose_raw` | [1,312] |
| 1 | `smplx_scale` | [1,6] |
| 2 | `smplx_shape` | [1,200] |
| 3 | `smplx_expression` | [1,50] |
| 4 | `flame_pose` | [1,14] |
| 5 | `flame_shape` | [1,300] |
| 6 | `flame_expression` | [1,50] |
| 7 | `camera_raw` | [1,3] |

`smplx_pose_raw` splits as:

```text
0:6       global_orient
6:132     body_pose,       21 × 6D
132:222   left_hand_pose,  15 × 6D
222:312   right_hand_pose, 15 × 6D
```

The 6D values are **not** Euler angles: decode to rotation matrices first, then to
axis-angle radians if needed.

### Preprocessing contract (`pear-rgb-crop256-bgr-imagenet-v1`)

```text
person bbox xyxy in source-image coordinates
→ centre (cx, cy), side = max(w, h) × 1.25
→ square affine crop (cv2.INTER_LINEAR, BORDER_CONSTANT 0)
→ 256×256 BGR
→ NCHW float32, divided by 255
→ ImageNet normalize (mean 0.485/0.456/0.406, std 0.229/0.224/0.225)
→ width slice [:, :, :, 32:-32]
```

The backbone therefore sees 256×192 content. Crops must be taken in source-video
coordinates, not in YOLOX's 640×640 letterbox space.

## Source Model

This bundle's torch weights (`assets/pear_model.pt`) are the upstream PEAR checkpoint,
unmodified:

- **Model weights (HuggingFace):** [BestWJH/PEAR_models](https://huggingface.co/BestWJH/PEAR_models) — `pear_model.pt`, fetched by upstream code via `hf_hub_download(repo_id="BestWJH/PEAR_models", filename="pear_model.pt")`
- **Project page:** <https://wujh2001.github.io/PEAR/>
- `pear_model.pt` — 2,685,908,343 bytes, sha256 `be82dfa06e7b0608c6440058dfa0794d9b2ceee69f6e5b09bf41bb0076abeb18`

Upstream states this is the *initial* release of the PEAR model rather than the final version
presented in the paper; it may underperform on complex poses.

### Source code

```bash
git clone https://github.com/Pixel-Talk/PEAR.git
git -C PEAR checkout 230fa1534367c9f357c1c192a328cdc87ab4491c
```

- **Repository:** <https://github.com/Pixel-Talk/PEAR> (Apache-2.0)
- **Commit:** `230fa1534367c9f357c1c192a328cdc87ab4491c` on `main` — 2026-08-01, *"Update app.py"*
- The clone used for export carries no submodules (upstream has no `.gitmodules`) and no
  local patches; the working tree differs from that commit only in file permission bits.

The Ascend OM was converted from those weights via ONNX with external data
(`pear_parameter_network_bs1.onnx` + `.data`, consolidated `.data` sha256
`76d0b08fea2a17133aa62b718e1faaf329e0167a42638ffe63c177951b5ab766`), with ATC
`--soc_version=Ascend310P1`. The OM (`pear_parameter_network_bs1.om`, sha256
`67798d9f1da61fba4e5b706b20acf82cf2daf030a3239f89709e655421c84e81`) is not re-trained.

## Assets Not Included

Consuming the parameter outputs (rotation decode, parameter bookkeeping) needs `numpy` only.

Reconstructing meshes / 3D joints additionally requires the SMPL-X, FLAME and MANO body
models, which are **not** redistributed here because their licenses do not permit it.
Obtain them yourself from the original sites and accept their terms:

- SMPL-X — <https://smpl-x.is.tue.mpg.de/> (`SMPLX_NEUTRAL_2020.npz`)
- FLAME 2020 — <https://flame.is.tue.mpg.de/> (`generic_model.pkl`)
- MANO — <https://mano.is.tue.mpg.de/> (`MANO_LEFT.pkl`, `MANO_RIGHT.pkl`)

These are research-licensed assets and are generally not usable for commercial deployment
without a separate agreement.

## Validation

Board evidence recorded on a real Ascend 310P1 over a 368-frame, 30 FPS clip
(`npu-smi info` SoC = `Ascend310P1`; `Ascend310P3` is not a valid target for this device).

Numerical alignment vs. the PyTorch reference (strict gate passed):

```text
raw parameter max abs diff:   0.036261   (gate 0.05)
rotation worst P95:           0.068421°  (gate 1.0°)
body joint max:               0.332952°  (gate 5.0°)
left  hand joint max:         0.211992°  (gate 5.0°)
right hand joint max:         0.211410°  (gate 5.0°)
left/right swap:              none (direct mean 0.027°, swapped mean 45.964°)
NaN/Inf:                      none; rotation matrices orthonormal to ~1e-7
```

Latency, same run:

```text
OM only:                mean 26.298 ms, P95 27.559 ms, max 31.641 ms
full ACL:               mean 27.784 ms, P95 29.602 ms, max 33.094 ms
ACL + CPU parameter processing: mean 28.542 ms, P95 30.542 ms, max 33.929 ms
```

**Status: engineering `GO`, strict real-time `CONDITIONAL`.** Against a 33.33 ms budget at
30 Hz, mean/P50/P95 fit, but the worst frame exceeds it by ~0.60 ms. Systems with a hard
per-frame deadline must budget for that overrun.

### Semantics caveats

- Pose outputs are SMPL-X **local joint rotations**, not robot motor angles. Driving a robot
  additionally requires a SMPL-X→joint mapping, axis transforms, zero offsets, sign and unit
  conversion, joint limits and velocity/acceleration limits — none of which are in this bundle.
- `camera_raw` and the recovered body are in relative/model coordinates. Without real camera
  intrinsics and root depth they must not be presented as absolute camera XYZ.

## Usage

Select the `ascend_310p` deployment through the IB-Robot unified inference runtime; the
bundle is consumed as an external model bundle (it is not stored in the IB-Robot Git
repository).

```python
from inference_manifest import load_inference_manifest

validated = load_inference_manifest("models/pear_parameter_network", "ascend_310p")
```

## License

Code and packaging: Apache-2.0. The PEAR weights are redistributed under the upstream
Apache-2.0 license of [BestWJH/PEAR_models](https://huggingface.co/BestWJH/PEAR_models) /
[Pixel-Talk/PEAR](https://github.com/Pixel-Talk/PEAR). The SMPL-X / FLAME / MANO body models
needed for mesh reconstruction are **not** included and carry their own restrictive licenses.

## Citation

    @misc{wu2026pear,
      title         = {PEAR: Pixel-aligned Expressive humAn mesh Recovery},
      author        = {Jiahao Wu and Yunfei Liu and Lijian Lin and Ye Zhu and Lei Zhu and Jingyi Li and Yu Li},
      year          = {2026},
      eprint        = {2601.22693},
      archivePrefix = {arXiv},
      primaryClass  = {cs.CV},
      url           = {https://arxiv.org/abs/2601.22693}
    }
    @software{ib_robot,
      title   = {IB-Robot: Intelligence Boom Robot},
      url     = {https://atomgit.com/openeuler/IB_Robot},
      license = {Apache-2.0}
    }