Pre-training is playing an increasingly important role in learning generic
feature representation for Person Re-identification (ReID). We argue that a
high-quality ReID representation should have three properties, namely,
multi-level awareness, occlusion robustness, and cross-region invariance. To
this end, we propose a simple yet effective pre-training framework, namely
PersonMAE, which involves two core designs into masked autoencoders to better
serve the task of Person Re-ID. 1) PersonMAE generates two regions from the
given image with RegionA as the input and \textit{RegionB} as the prediction
target. RegionA is corrupted with block-wise masking to mimic common occlusion
in ReID and its remaining visible parts are fed into the encoder. 2) Then
PersonMAE aims to predict the whole RegionB at both pixel level and semantic
feature level. It encourages its pre-trained feature representations with the
three properties mentioned above. These properties make PersonMAE compatible
with downstream Person ReID tasks, leading to state-of-the-art performance on
four downstream ReID tasks, i.e., supervised (holistic and occluded setting),
and unsupervised (UDA and USL setting). Notably, on the commonly adopted
supervised setting, PersonMAE with ViT-B backbone achieves 79.8% and 69.5% mAP
on the MSMT17 and OccDuke datasets, surpassing the previous state-of-the-art by
a large margin of +8.0 mAP, and +5.3 mAP, respectively.

我们提出了一个简洁而有效的预训练框架，名为 PersonMAE，通过引入两个核心设计到遮挡自编码器中，以更好地为 Person Re-ID 任务提供服务，并在四个下游任务中实现了最先进的性能。

PersonMAE: 使用遮罩自编码器进行人物再识别预训练

PersonMAE: Person Re-Identification Pre-Training with Masked  AutoEncoders

Regression-based methods for 3D human pose estimation directly predict the 3D
pose parameters from a 2D image using deep networks. While achieving
state-of-the-art performance on standard benchmarks, their performance degrades
under occlusion. In contrast, optimization-based methods fit a parametric body
model to 2D features in an iterative manner. The localized reconstruction loss
can potentially make them robust to occlusion, but they suffer from the 2D-3D
ambiguity.
Motivated by the recent success of generative models in rigid object pose
estimation, we propose 3D-aware Neural Body Fitting (3DNBF) - an approximate
analysis-by-synthesis approach to 3D human pose estimation with SOTA
performance and occlusion robustness. In particular, we propose a generative
model of deep features based on a volumetric human representation with Gaussian
ellipsoidal kernels emitting 3D pose-dependent feature vectors. The neural
features are trained with contrastive learning to become 3D-aware and hence to
overcome the 2D-3D ambiguity.
Experiments show that 3DNBF outperforms other approaches on both occluded and
standard benchmarks. Code is available at this https URL

基于生成模型的 3D 人体姿势拟合（3DNBF）方法，在处理遮挡情况下具备最佳表现，通过训练使神经特征具备三维感知，克服了二维与三维之间的模糊性。