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 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.

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

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

Outputs: eight tensors, fixed order

smplx_pose_raw splits as:

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)

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 — 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

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):

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:

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).

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 / 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}
}