Model pre-training is essential in human-centric perception. In this paper,
we first introduce masked image modeling (MIM) as a pre-training approach for
this task. Upon revisiting the MIM training strategy, we reveal that human
structure priors offer significant potential. Motivated by this insight, we
further incorporate an intuitive human structure prior - human parts - into
pre-training. Specifically, we employ this prior to guide the mask sampling
process. Image patches, corresponding to human part regions, have high priority
to be masked out. This encourages the model to concentrate more on body
structure information during pre-training, yielding substantial benefits across
a range of human-centric perception tasks. To further capture human
characteristics, we propose a structure-invariant alignment loss that enforces
different masked views, guided by the human part prior, to be closely aligned
for the same image. We term the entire method as HAP. HAP simply uses a plain
ViT as the encoder yet establishes new state-of-the-art performance on 11
human-centric benchmarks, and on-par result on one dataset. For example, HAP
achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K
for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose
estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.

模型预训练对以人为中心的感知至关重要。本文介绍了一种称为 MIM 的预训练方法，并在其基础上引入了人体结构先验，通过人体部位的引导来指导掩码采样过程，从而更好地捕捉人体结构信息。此外，我们还提出了一种结构不变的对齐损失，通过人体部位先验来引导不同的掩码视图在同一图像上紧密对齐。该方法命名为 HAP，使用简单的 ViT 编码器，在 11 个人体为中心的基准测试上取得了新的最高性能，一个数据集上与现有方法相当。例如，对于人员重新识别，HAP 在 MSMT17 上实现了 78.1% 的 mAP，在行人属性识别的 PA-100K 上实现了 86.54% 的 mA，在 MS COCO 上实现了 78.2% 的 AP，可以进行 2D 姿势估计，在 3DPW 上实现了 56.0 的 PA-MPJPE，可进行 3D 姿势和形状估计。