Unsupervised Cross-Domain 3D Human Pose Estimation via Pseudo-Label-Guided Global Transforms

• URL: http://arxiv.org/abs/2504.12699v1
• Date: Thu, 17 Apr 2025 06:57:20 GMT
• Title: Unsupervised Cross-Domain 3D Human Pose Estimation via Pseudo-Label-Guided Global Transforms
• Authors: Jingjing Liu, Zhiyong Wang, Xinyu Fan, Amirhossein Dadashzadeh, Honghai Liu, Majid Mirmehdi,
• Abstract summary: We propose a novel framework that conducts global transformations between pose positions.<n>A Pose Augmentor is incorporated to address variations in human posture and body size.<n>The proposed method outperforms state-of-the-art approaches and even outperforms the target-trained model.
• Score: 17.801307669723634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing 3D human pose estimation methods often suffer in performance, when applied to cross-scenario inference, due to domain shifts in characteristics such as camera viewpoint, position, posture, and body size. Among these factors, camera viewpoints and locations {have been shown} to contribute significantly to the domain gap by influencing the global positions of human poses. To address this, we propose a novel framework that explicitly conducts global transformations between pose positions in the camera coordinate systems of source and target domains. We start with a Pseudo-Label Generation Module that is applied to the 2D poses of the target dataset to generate pseudo-3D poses. Then, a Global Transformation Module leverages a human-centered coordinate system as a novel bridging mechanism to seamlessly align the positional orientations of poses across disparate domains, ensuring consistent spatial referencing. To further enhance generalization, a Pose Augmentor is incorporated to address variations in human posture and body size. This process is iterative, allowing refined pseudo-labels to progressively improve guidance for domain adaptation. Our method is evaluated on various cross-dataset benchmarks, including Human3.6M, MPI-INF-3DHP, and 3DPW. The proposed method outperforms state-of-the-art approaches and even outperforms the target-trained model.

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