Meet MVHumanNet: A Large-Scale Dataset that Comprises Multi-View Human Action Sequences of 4,500 Human Identities

Researchers from FNii CUHKSZ and SSE CUHKSZ have introduced MVHumanNet, a vast dataset for multi-view human action sequences with comprehensive annotations, such as human masks, camera parameters, 2D and 3D key points, SMPL/SMPLX parameters, and textual descriptions. MVHumanNet is poised to drive innovation in large-scale 3D human-centric tasks like action recognition, human NeRF reconstruction, and image generation.

 Meet MVHumanNet: A Large-Scale Dataset that Comprises Multi-View Human Action Sequences of 4,500 Human Identities

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MVHumanNet: A Large-Scale Dataset for Visual AI Tasks

Introduction

Researchers from FNii CUHKSZ, SSE CUHKSZ have introduced MVHumanNet, a comprehensive dataset for multi-view human action sequences. The dataset includes human masks, camera parameters, 2D and 3D key points, SMPL/SMPLX parameters, and textual descriptions. MVHumanNet aims to drive innovation in large-scale 3D human-centric tasks.

Key Features

MVHumanNet overcomes limitations of existing datasets by providing extensive annotations and support for research in 2D/3D human-centric tasks like action recognition, NeRF reconstruction, text-driven view-unconstrained human image generation, and 2D/3D avatar creation.

Value and Practical Solutions

With 4,500 identities, 9,000 outfits, and extensive annotations, MVHumanNet is a valuable resource for researchers and developers working on various visual tasks related to human-centric applications. The dataset has shown performance gains and effectiveness in diverse 2D and 3D visual tasks, including action recognition, NeRF-based reconstruction, text-driven image generation, and avatar creation.

Future Implications

MVHumanNet is expected to drive further innovations in action recognition, human NeRF reconstruction, text-driven image generation, and avatar creation. Future research recommends publicly releasing the dataset with annotations to provide a foundational resource for future research in the 3D digital human community.

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