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Human4K dataset advances 3D human reconstruction with 4K multi-view mocap data · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Human4K, a new large-scale dataset designed to improve 3D human reconstruction. This dataset features over six million 4K images captured from an eight-view camera system, synchronized with professional motion capture data. Human4K aims to address limitations in existing datasets by providing high-resolution images and precise annotations for complex, whole-body motions, including challenging scenarios like depth ambiguity and self-occlusion. Training models with Human4K has shown significant improvements in reconstructing human geometry, particularly for extremities like hands and feet. AI

IMPACT This dataset could significantly improve the accuracy and robustness of AI models used for 3D human reconstruction, particularly in challenging real-world scenarios.

RANK_REASON The cluster describes a new academic dataset and paper published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Human4K dataset advances 3D human reconstruction with 4K multi-view mocap data · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Tianshun Han, Ziyu Shi, Lijian Liu, Ajian Liu, Benjia Zhou, Hugo Jair Escalante, Yanyan Liang, Sergio Escalera, Zhen Lei, Jun Wan ·

    Human4K: A Large-Scale 4K Multi-View Mocap Dataset for Whole-Body 3D Human Reconstruction

    arXiv:2607.13646v1 Announce Type: cross Abstract: Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios. They often produce unstable geometry, inaccurate limb articulation and unrel…

  2. arXiv cs.AI TIER_1 English(EN) · Jun Wan ·

    Human4K: A Large-Scale 4K Multi-View Mocap Dataset for Whole-Body 3D Human Reconstruction

    Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios. They often produce unstable geometry, inaccurate limb articulation and unreliable predictions under depth ambiguity or self-oc…

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