MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human
  Captures
In this era, the success of large language models and text-to-image models
can be attributed to the driving force of large-scale datasets. However, in the
realm of 3D vision, while remarkable progress has been made with models trained
on large-scale synthetic and real-captured object data like Objaverse and
MVImgNet, a similar level of progress has not been observed in the domain of
human-centric tasks partially due to the lack of a large-scale human dataset.
Existing datasets of high-fidelity 3D human capture continue to be mid-sized
due to the significant challenges in acquiring large-scale high-quality 3D
human data. To bridge this gap, we present MVHumanNet, a dataset that comprises
multi-view human action sequences of 4,500 human identities. The primary focus
of our work is on collecting human data that features a large number of diverse
identities and everyday clothing using a multi-view human capture system, which
facilitates easily scalable data collection. Our dataset contains 9,000 daily
outfits, 60,000 motion sequences and 645 million frames with extensive
annotations, including human masks, camera parameters, 2D and 3D keypoints,
SMPL/SMPLX parameters, and corresponding textual descriptions. To explore the
potential of MVHumanNet in various 2D and 3D visual tasks, we conducted pilot
studies on view-consistent action recognition, human NeRF reconstruction,
text-driven view-unconstrained human image generation, as well as 2D
view-unconstrained human image and 3D avatar generation. Extensive experiments
demonstrate the performance improvements and effective applications enabled by
the scale provided by MVHumanNet. As the current largest-scale 3D human
dataset, we hope that the release of MVHumanNet data with annotations will
foster further innovations in the domain of 3D human-centric tasks at scale.
656fe0b8d81a20af9c201de7
Zhangyang Xiong
656fe0b8d81a20af9c201de8
Chenghong Li
admin_assigned
2023-12-06T12:06:18.033Z
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Chenghong Li
user
kevinlee09
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Kenkun Liu
656fe0b8d81a20af9c201dea
Hongjie Liao
claimed_verified
2025-05-08T09:00:48.454Z
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/avatars/c3c8a00f1e1d89bee86e6cebe9f5902e.svg
JesseLiao
user
JesseLiao
656fe0b8d81a20af9c201deb
Jianqiao Hu
656fe0b8d81a20af9c201dec
Junyi Zhu
admin_assigned
2023-12-06T12:07:36.548Z
6477b2038ab7e732b6d8a9b5
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Junyi Zhu
user
JJZhu-AI
656fe0b8d81a20af9c201ded
Shuliang Ning
admin_assigned
2023-12-06T12:07:46.456Z
6358d973f53b06a81271d8d6
/avatars/06b7e205a1b08d2419afc2f6273028d1.svg
Ning
user
Shuliang
656fe0b8d81a20af9c201dee
Lingteng Qiu
admin_assigned
2023-12-06T12:08:03.033Z
6409c086f3dabf9382484765
/avatars/5871f3223193b85aa61c7037eebcc691.svg
Qiu
user
Lingteng
656fe0b8d81a20af9c201def
Chongjie Wang
656fe0b8d81a20af9c201df0
Shijie Wang
656fe0b8d81a20af9c201df1
Shuguang Cui
656fe0b8d81a20af9c201df2
Xiaoguang Han
MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human
  Captures
In this era, the success of large language models and text-to-image models
can be attributed to the driving force of large-scale datasets. However, in the
realm of 3D vision, while remarkable progress has been made with models trained
on large-scale synthetic and real-captured object data like Objaverse and
MVImgNet, a similar level of progress has not been observed in the domain of
human-centric tasks partially due to the lack of a large-scale human dataset.
Existing datasets of high-fidelity 3D human capture continue to be mid-sized
due to the significant challenges in acquiring large-scale high-quality 3D
human data. To bridge this gap, we present MVHumanNet, a dataset that comprises
multi-view human action sequences of 4,500 human identities. The primary focus
of our work is on collecting human data that features a large number of diverse
identities and everyday clothing using a multi-view human capture system, which
facilitates easily scalable data collection. Our dataset contains 9,000 daily
outfits, 60,000 motion sequences and 645 million frames with extensive
annotations, including human masks, camera parameters, 2D and 3D keypoints,
SMPL/SMPLX parameters, and corresponding textual descriptions. To explore the
potential of MVHumanNet in various 2D and 3D visual tasks, we conducted pilot
studies on view-consistent action recognition, human NeRF reconstruction,
text-driven view-unconstrained human image generation, as well as 2D
view-unconstrained human image and 3D avatar generation. Extensive experiments
demonstrate the performance improvements and effective applications enabled by
the scale provided by MVHumanNet. As the current largest-scale 3D human
dataset, we hope that the release of MVHumanNet data with annotations will
foster further innovations in the domain of 3D human-centric tasks at scale.
MVHumanNet, a large-scale dataset of multi-view human action sequences, enhances performance and applications in 2D and 3D visual tasks.
multi-view human capture SMPL/SMPLX parameters NeRF reconstruction text-driven generation