---
title: FreiHAND
canonical_url: "https://www.modelscope.cn/datasets/OmniData/FreiHAND"
md_url: "https://www.modelscope.cn/datasets/OmniData/FreiHAND.md"
repository: OmniData/FreiHAND
last_updated: 2024-07-10
license: "[FreiHAND Custom]"
storage_size: "3.6 GB"
domain:
  - publishDate
  - displayName
  - paperUrl
  - publishUrl
  - mediaTypes
  - labelTypes
  - taskTypes
  - publisher
tasks:
  - 2019-01-01
  - FreiHAND
  - "https://arxiv.org/pdf/1909.04349v3.pdf"
  - "https://lmb.informatik.uni-freiburg.de/projects/freihand/"
  - Image
  - Keypoints3D
  - "Pose Estimation"
  - "University of Freiburg"
downloads: 138
stars: 0
---

# FreiHAND

> FreiHAND - OmniData 在 ModelScope 开源的数据集。displayName: FreiHAND labelTypes: SemanticMask Keypoints3D license: FreiHAND Custom mediaTypes: Image paperUrl: https://arxiv.org/pdf/1909.04349v3.pdf publishDate: "2019-01-01" publishUrl:…

OmniData/FreiHAND 是 ModelScope 魔搭社区上的2019-01-01、FreiHAND、https://arxiv.org/pdf/1909.04349v3.pdf数据集，涉及 publishDate、displayName、paperUrl 领域，存储大小 3.6 GB，采用 [FreiHAND Custom] 许可。

- **Repository**: OmniData/FreiHAND
- **License**: [FreiHAND Custom]
- **Tasks**: 2019-01-01, FreiHAND, https://arxiv.org/pdf/1909.04349v3.pdf, https://lmb.informatik.uni-freiburg.de/projects/freihand/, Image, Keypoints3D, Pose Estimation, University of Freiburg
- **Domain**: publishDate, displayName, paperUrl, publishUrl, mediaTypes, labelTypes, taskTypes, publisher
- **Storage size**: 3.6 GB
- **Downloads**: 138
- **Stars**: 0
- **Last updated**: 2024-07-10

Source: https://www.modelscope.cn/datasets/OmniData/FreiHAND

---

displayName: FreiHAND
labelTypes:
- SemanticMask
- Keypoints3D
license:
- FreiHAND Custom
mediaTypes:
- Image
paperUrl: https://arxiv.org/pdf/1909.04349v3.pdf
publishDate: "2019-01-01"
publishUrl: https://lmb.informatik.uni-freiburg.de/projects/freihand/
publisher:
- Adobe Research
- University of Freiburg
tags: []
taskTypes:
- Pose Estimation

---
  ## 简介
  从单个 RGB 图像估计 3D 手部姿势是一个高度模糊的问题，它依赖于无偏的训练数据集。在本文中，我们分析了在现有数据集上进行训练时的跨数据集泛化。我们发现这些方法在他们训练的数据集上表现良好，但不能推广到其他数据集或野外场景。因此，我们引入了第一个伴随 3D 手部姿势和形状注释的大规模多视图手部数据集。为了注释这个真实世界的数据集，我们提出了一种迭代的、半自动的“人在环”方法，其中包括手部拟合优化，以推断每个样本的 3D 姿势和形状。我们表明，在我们的数据集上训练的方法在其他数据集上测试时始终表现良好。此外，该数据集允许我们训练一个网络，该网络可以从单个 RGB 图像中预测完整的关节手形。
  ## 引文
  ```
@inproceedings{zimmermann2019freihand,
  title={Freihand: A dataset for markerless capture of hand pose and shape from single rgb images},
  author={Zimmermann, Christian and Ceylan, Duygu and Yang, Jimei and Russell, Bryan and Argus, Max and Brox, Thomas},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={813--822},
  year={2019}
}
```
  
## Download dataset
:modelscope-code[]{type="git"}