PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space Model

• URL: http://arxiv.org/abs/2408.03540v1
• Date: Wed, 7 Aug 2024 04:38:03 GMT
• Title: PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space Model
• Authors: Yunlong Huang, Junshuo Liu, Ke Xian, Robert Caiming Qiu,
• Abstract summary: We propose a purely SSM-based approach with linear correlations for complexityD human pose estimation in monocular video video. Specifically, we propose a bidirectional global temporal-local-temporal block that comprehensively models human joint relations within individual frames as well as across frames. This strategy provides a more logical geometric ordering strategy, resulting in a combined-local spatial scan.
• Score: 7.286873011001679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidirectional modeling of spatio-temporal relationships, and insufficient learning of spatial-temporal correlations. Recently, the Mamba architecture, utilizing the state space model (SSM), has exhibited superior long-range modeling capabilities in a variety of vision tasks with linear complexity. In this paper, we propose PoseMamba, a novel purely SSM-based approach with linear complexity for 3D human pose estimation in monocular video. Specifically, we propose a bidirectional global-local spatio-temporal SSM block that comprehensively models human joint relations within individual frames as well as temporal correlations across frames. Within this bidirectional global-local spatio-temporal SSM block, we introduce a reordering strategy to enhance the local modeling capability of the SSM. This strategy provides a more logical geometric scanning order and integrates it with the global SSM, resulting in a combined global-local spatial scan. We have quantitatively and qualitatively evaluated our approach using two benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments demonstrate that PoseMamba achieves state-of-the-art performance on both datasets while maintaining a smaller model size and reducing computational costs. The code and models will be released.

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