title: MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human
    Captures
summary: 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.
paper_authors._id: 656fe0b8d81a20af9c201de7
paper_authors.name: Zhangyang Xiong
paper_authors._id: 656fe0b8d81a20af9c201de8
paper_authors.name: Chenghong Li
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/f9f1b19a4d2e2453a84971c807ed3af3.svg
paper_authors.fullname: Chenghong Li
paper_authors.type: user
paper_authors.user: kevinlee09
paper_authors._id: 656fe0b8d81a20af9c201de9
paper_authors.name: Kenkun Liu
paper_authors._id: 656fe0b8d81a20af9c201dea
paper_authors.name: Hongjie Liao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-08T09:00:48.454Z
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paper_authors.avatarUrl: /avatars/c3c8a00f1e1d89bee86e6cebe9f5902e.svg
paper_authors.fullname: JesseLiao
paper_authors.type: user
paper_authors.user: JesseLiao
paper_authors._id: 656fe0b8d81a20af9c201deb
paper_authors.name: Jianqiao Hu
paper_authors._id: 656fe0b8d81a20af9c201dec
paper_authors.name: Junyi Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-06T12:07:36.548Z
paper_authors._id: 6477b2038ab7e732b6d8a9b5
paper_authors.avatarUrl: /avatars/c859a35b8965a904f103bbc34f36ab2a.svg
paper_authors.fullname: Junyi Zhu
paper_authors.type: user
paper_authors.user: RyanZhu
paper_authors._id: 656fe0b8d81a20af9c201ded
paper_authors.name: Shuliang Ning
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/06b7e205a1b08d2419afc2f6273028d1.svg
paper_authors.fullname: Ning
paper_authors.type: user
paper_authors.user: Shuliang
paper_authors._id: 656fe0b8d81a20af9c201dee
paper_authors.name: Lingteng Qiu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-06T12:08:03.033Z
paper_authors._id: 6409c086f3dabf9382484765
paper_authors.avatarUrl: /avatars/5871f3223193b85aa61c7037eebcc691.svg
paper_authors.fullname: Qiu
paper_authors.type: user
paper_authors.user: Lingteng
paper_authors._id: 656fe0b8d81a20af9c201def
paper_authors.name: Chongjie Wang
paper_authors._id: 656fe0b8d81a20af9c201df0
paper_authors.name: Shijie Wang
paper_authors._id: 656fe0b8d81a20af9c201df1
paper_authors.name: Shuguang Cui
paper_authors._id: 656fe0b8d81a20af9c201df2
paper_authors.name: Xiaoguang Han
paper_title: MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human
    Captures
paper_summary: 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.
paper_ai_summary: MVHumanNet, a large-scale dataset of multi-view human action sequences, enhances performance and applications in 2D and 3D visual tasks.
paper_ai_keywords: multi-view human capture SMPL/SMPLX parameters NeRF reconstruction text-driven generation