HYPERPOSE: Hyperbolic Kinematic Phase-Space Attention for 3D Human Pose Estimation
Researchers have introduced HYPERPOSE, a novel framework for 3D human pose estimation that operates entirely within the Lorentz model of hyperbolic space. Unlike current state-of-the-art methods relying on transformers and graph convolutional networks in Euclidean space, which often suffer from volume distortion due to mismatches with the human skeleton's tree structure, HYPERPOSE preserves hierarchical topology natively. The framework utilizes Hyperbolic Kinematic Phase-Space Attention (HKPSA) to embed joint relationships without distortion and employs a multi-scale windowed hyperbolic attention mechanism for efficient temporal dynamics modeling. To address training instability in non-Euclidean manifolds, it incorporates a new Riemannian loss suite and an uncertainty-weighted curriculum enforcing physical constraints like bone length. Evaluations on Human3.6M and MPI-INF-3DHP datasets demonstrate that HYPERPOSE achieves superior structural and temporal coherence, significantly reducing velocity errors and establishing new benchmarks in positional accuracy.
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HYPERPOSE: Hyperbolic Kinematic Phase-Space Attention for 3D Human Pose Estimation
Researchers have introduced HYPERPOSE, a novel framework for 3D human pose estimation that operates entirely within the Lorentz model of hyperbolic space. Unlike current state-of-the-art methods relying on transformers and graph convolutional networks in Euclidean space, which often suffer from volume distortion due to mismatches with the human skeleton's tree structure, HYPERPOSE preserves hierarchical topology natively. The framework utilizes Hyperbolic Kinematic Phase-Space Attention (HKPSA) to embed joint relationships without distortion and employs a multi-scale windowed hyperbolic attention mechanism for efficient temporal dynamics modeling. To address training instability in non-Euclidean manifolds, it incorporates a new Riemannian loss suite and an uncertainty-weighted curriculum enforcing physical constraints like bone length. Evaluations on Human3.6M and MPI-INF-3DHP datasets demonstrate that HYPERPOSE achieves superior structural and temporal coherence, significantly reducing velocity errors and establishing new benchmarks in positional accuracy.
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