Abstract
3D Human Pose Estimation (3D HPE) is vital in various applications, from person re-identification and action recognition to virtual reality. However, the reliance on annotated 3D data collected in controlled environments poses challenges for generalization to diverse in-the-wild scenarios. Existing domain adaptation (DA) paradigms like general DA and source-free DA for 3D HPE overlook the issues of non-stationary target pose datasets. To address these challenges, we propose a novel task named lifelong domain adaptive 3D HPE. To our knowledge, we are the first to introduce the lifelong domain adaptation to the 3D HPE task. In this lifelong DA setting, the pose estimator is pretrained on the source domain and subsequently adapted to distinct target domains. Moreover, during adaptation to the current target domain, the pose estimator cannot access the source and all the previous target domains. The lifelong DA for 3D HPE involves overcoming challenges in adapting to current domain poses and preserving knowledge from previous domains, particularly combating catastrophic forgetting. We present an innovative Generative Adversarial Network (GAN) framework, which incorporates 3D pose generators, a 2D pose discriminator, and a 3D pose estimator. This framework effectively mitigates domain shifts and aligns original and augmented poses. Moreover, we construct a novel 3D pose generator paradigm, integrating pose-aware, temporal-aware, and domain-aware knowledge to enhance the current domain's adaptation and alleviate catastrophic forgetting on previous domains. Our method demonstrates superior performance through extensive experiments on diverse domain adaptive 3D HPE datasets.
Abstract (translated)
三维人体姿态估计（3D HPE）在众多应用中至关重要，从人员再识别和动作识别到虚拟现实。然而，在受控环境中收集的标注3D数据依赖性导致了将其推广至多样化的真实场景中的挑战。现有的域适应（DA）范式，例如针对3D HPE的一般域适应和无源域适应，忽略了目标姿态数据集的非平稳性问题。为了解决这些挑战，我们提出了一项名为终身域适配的三维人体姿态估计的新任务。据我们所知，这是我们首次将终身域适应应用于三维人体姿态估计任务中。在这种终身DA设置下，姿态估计器首先在源域上进行预训练，随后逐步调整以适应不同的目标域。此外，在当前目标域的调适过程中，姿态估计器无法访问源域和所有之前的target domains。 针对3D HPE的终身DA需要克服当前领域的姿势适应挑战，并保留来自先前领域知识的问题，尤其是要解决灾难性遗忘问题。为此，我们提出了一种创新性的生成对抗网络（GAN）框架，该框架结合了三维姿态生成器、二维姿态判别器和三维姿态估计器。此框架有效地缓解了域偏移并使原始姿势与增强后的姿势对齐。 此外，我们构建了一个新颖的3D姿态生成器模式，整合了具有姿态感知、时间感知和领域感知的知识，以提升当前领域的适应性，并减轻在先前领域的灾难性遗忘问题。通过广泛的实验验证，在多样化的三维域适配人体姿态估计数据集上，我们的方法展现出了卓越的表现。
URL
https://arxiv.org/abs/2512.23860
