<br />

- **Description**:

AFLW2000-3D is a dataset of 2000 images that have been annotated with
image-level 68-point 3D facial landmarks. This dataset is typically used for
evaluation of 3D facial landmark detection models. The head poses are very
diverse and often hard to be detected by a cnn-based face detector. The 2D
landmarks are skipped in this dataset, since some of the data are not consistent
to 21 points, as the original paper mentioned.

- **Additional Documentation** :
  [Explore on Papers With Code](https://paperswithcode.com/dataset/aflw2000-3d)

- **Homepage** :
  <http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm>

- **Source code** :
  [`tfds.datasets.aflw2k3d.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/aflw2k3d/aflw2k3d_dataset_builder.py)

- **Versions**:

  - **`1.0.0`** (default): No release notes.
- **Download size** : `83.36 MiB`

- **Dataset size** : `42.48 MiB`

- **Auto-cached**
  ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
  Yes

- **Splits**:

| Split | Examples |
|---|---|
| `'train'` | 2,000 |

- **Feature structure**:

    FeaturesDict({
        'image': Image(shape=(450, 450, 3), dtype=uint8),
        'landmarks_68_3d_xy_normalized': Tensor(shape=(68, 2), dtype=float32),
        'landmarks_68_3d_z': Tensor(shape=(68, 1), dtype=float32),
    })

- **Feature documentation**:

| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
|   | FeaturesDict |   |   |   |
| image | Image | (450, 450, 3) | uint8 |   |
| landmarks_68_3d_xy_normalized | Tensor | (68, 2) | float32 |   |
| landmarks_68_3d_z | Tensor | (68, 1) | float32 |   |

- **Supervised keys** (See
  [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
  `None`

- **Figure**
  ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):

![Visualization](https://storage.googleapis.com/tfds-data/visualization/fig/aflw2k3d-1.0.0.png)

- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):

<iframe src="https:///frame/datasets/catalog/aflw2k3d_fdfa4025a9e90cd4796917cdf62abf42d3168d51c6476cc1e1c8baa9ae910a82.frame" class="framebox inherit-locale " allow="clipboard-write https://" allowfullscreen is-upgraded></iframe>

- **Citation**:

    @article{DBLP:journals/corr/ZhuLLSL15,
      author    = {Xiangyu Zhu and
                   Zhen Lei and
                   Xiaoming Liu and
                   Hailin Shi and
                   Stan Z. Li},
      title     = {Face Alignment Across Large Poses: {A} 3D Solution},
      journal   = {CoRR},
      volume    = {abs/1511.07212},
      year      = {2015},
      url       = {http://arxiv.org/abs/1511.07212},
      archivePrefix = {arXiv},
      eprint    = {1511.07212},
      timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
      biburl    = {https://dblp.org/rec/bib/journals/corr/ZhuLLSL15},
      bibsource = {dblp computer science bibliography, https://dblp.org}
    }