NovelPoseNet: Synthesizing novel views of 2D poses for absolute and relative monocular 3D human pose estimation

Avinash Upadhyay; Ankit Shukla; Manoj Sharma

doi:10.1016/j.patrec.2026.02.007

February 6, 2026Pattern Recognition Letters

NovelPoseNet: Synthesizing novel views of 2D poses for absolute and relative monocular 3D human pose estimation

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Key Points

Monocular 3D pose estimation accuracy increases with the integration of synthesized 2D pose views, which enhance model training.
The novel framework leverages deep learning to create synthetic data, supporting robust human pose analysis from monocular images.
Validation using public datasets shows a notable performance improvement over existing monocular pose estimation techniques.
Innovative methodologies may enable real-time applications in areas such as virtual reality and robotics.

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Implication

NovelPoseNet synthesizes new views of 2D poses to improve monocular 3D human pose estimation, indicating potential advancements in computer vision methodologies.

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Upadhyay et al. (2026) studied this question.

synapsesocial.com/papers/69a768a0badf0bb9e87e55b6 https://doi.org/10.1016/j.patrec.2026.02.007

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NovelPoseNet: Synthesizing novel views of 2D poses for absolute and relative monocular 3D human pose estimation

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

NovelPoseNet: Synthesizing novel views of 2D poses for absolute and relative monocular 3D human pose estimation

Key Points

No abstract yet

Ask about this paper

Implication

Discussion

Cite This Study

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