Awesome

PoseDA

Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation
Wenhao Chai, Zhongyu Jiang, Jenq-Neng Hwang, Gaoang Wang✉️
ICCV 2023

Here I provide the core code for the proposed method, which can be easily merged into any existing code framework.

Global Position Alignment

Global position alignment is designed to eliminate the domain gap in viewpoints, which is simple yet efficient. After that, the scale and location distributions of the 2D poses of the source dataset can be migrated to distributions of target dataset.

def gpa(source_3d, target_2d, camera_params):
    """
    source_3d [N x J x 3]
        3D poses in source domain in meter
    target_2d [N x J x 2]
        2D poses in target domain after screen normalization (-1, 1)
    camera_params [N x 4]
        in order cx, cy, fx, fy
        processed by (fx, cx) = (fx, cx) * 2 / w
                     (fy, cy) = (fy, cy) * 2 / h
        i.e. in 3DHP fx, fy ~= 1.5; cx, cy ~= 0
    """
    assert source_3d.shape[:2] == target_2d.shape[:2], "poses should have same size"

    # create pairs randomly
    index = torch.randperm(target_2d.shape[0])
    target_2d = target_2d[index]
    
    # calculate 2d box
    w = torch.max(target_2d[..., 0], dim=-1)[0] - torch.min(target_2d[..., 0], dim=-1)[0]
    h = torch.max(target_2d[..., 1], dim=-1)[0] - torch.min(target_2d[..., 1], dim=-1)[0]
    s = (w + h) / 2
    
    # calculate 3d box
    dx = torch.max(source_3d[..., 0], dim=-1)[0] - torch.min(source_3d[..., 0], dim=-1)[0]
    dy = torch.max(source_3d[..., 1], dim=-1)[0] - torch.min(source_3d[..., 1], dim=-1)[0]
    
    # calculate z
    fx, fy = camera_params[0, :2]
    z = (fx * dx + fy * dy) / (2 * s)
    
    # process with camera params
    target_2d[..., 0, :] -= camera_params[..., 2:4] # c
    target_2d[..., 0, :] /= camera_params[..., :2] # f

    u, v = target_2d[..., 0, 0], target_2d[..., 0, 1]

    # calculate x, y
    x, y = z * u, z * v
    x, y, z = x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)
    
    # give a new position to source data
    position = torch.stack([x, y, z], dim=1).reshape(-1, 1, 3)
    source_3d = source_3d - source_3d[:, :1, :] + position
    
    return source_3d

Results

We show performance boosting in various backbone (mlp, conv, gcn). Source domain: Human3.6M, target domain: MPI-INF-3DHP.

Method	MPJPE ($\downarrow$)	PCK ($\uparrow$)	AUC ($\uparrow$)
SemGCN	95.96	80.68	48.48
+ GPA	86.56 (-9.4)	83.85 (+3.2)	50.98 (+2.5)
SimpleBaseline	81.23	85.85	53.95
+ GPA	69.19 (-12.0)	89.90 (+4.1)	58.50 (+4.6)
ST-GCN	81.19	85.92	53.78
+ GPA	74.41 (-6.8)	88.58 (+2.7)	55.52 (+1.7)
VideoPose3D	82.55	85.71	53.35
+ GPA	66.07 (-16.5)	90.87 (+5.2)	60.07 (+6.7)

The distribution visualization before and after GPA.

distribution

Citation

If our work is useful for your research, please consider citing:

@article{chai2023global,
  title={Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation},
  author={Chai, Wenhao and Jiang, Zhongyu and Hwang, Jenq-Neng and Wang, Gaoang},
  journal={arXiv preprint arXiv:2303.16456},
  year={2023}
}

Projects based on PoseDA

PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation
Hanbing Liu, Jun-Yan He, Zhi-Qi Cheng, Wangmeng Xiang, Qize Yang, Wenhao Chai, Gaoang Wang, Xu Bao, Bin Luo, Yifeng Geng, Xuansong Xie
ACM MM 2023