Datasets:
File size: 4,318 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: output_mask
dtype: image
- name: glb_3d_file
dtype: string
splits:
- name: train
num_bytes: 5964735332
num_examples: 3860
download_size: 5964790359
dataset_size: 5964735332
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- 3d-face
- female
- depth-estimation
- face-depth-estimation
- face-reconstruction
- synthetic-data
- image-processing
- computer-vision
- facial-depth
license: apache-2.0
task_categories:
- image-to-3d
language:
- en
size_categories:
- 1K<n<10K
---
## **Female-Face-Depth-3D**
**Female-Face-Depth-3D** is a high-quality dataset designed for **female face depth estimation and 3D face reconstruction**. The dataset contains paired RGB face images, dense facial depth maps, and corresponding 3D meshes in GLB format, making it suitable for training and evaluating modern computer vision and image-to-3D models. Every sample provides a direct correspondence between a facial photograph, its reconstructed depth representation, and an associated 3D mesh, enabling end-to-end learning for geometry-aware tasks. The dataset is intended for research and development involving monocular depth estimation, neural rendering, facial geometry prediction, 3D reconstruction, synthetic data generation, and multimodal vision systems. It is distributed in the Hugging Face Datasets format using optimized Parquet files for efficient loading while the associated GLB meshes can be downloaded individually when needed.
## Dataset Statistics
| Property | Value |
|-----------|-------|
| Number of Samples | 3,860 |
| Image Format | RGB |
| 3D Mesh Format | GLB |
| Dataset Format | Optimized Parquet |
## Dataset Structure
Each sample contains three fields:
| Column | Type | Description |
|---------|------|-------------|
| `image` | Image | Original RGB face image |
| `output_mask` | Image | Dense facial depth map |
| `glb_3d_file` | String | Filename of the corresponding GLB mesh |
Example:
```python
sample = ds[0]
print(sample.keys())
# dict_keys([
# "image",
# "output_mask",
# "glb_3d_file"
# ])
```
## Loading the Dataset
```python
from datasets import load_dataset
ds = load_dataset(
"prithivMLmods/Female-Face-Depth-3D",
split="train"
)
```
## Example Usage
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_download
import matplotlib.pyplot as plt
ds = load_dataset("prithivMLmods/Female-Face-Depth-3D", split="train")
sample = ds[0]
image = sample["image"]
mask = sample["output_mask"]
glb_name = sample["glb_3d_file"]
print("GLB filename:", glb_name)
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
axes[0].imshow(image)
axes[0].set_title("Image")
axes[0].axis("off")
axes[1].imshow(mask)
axes[1].set_title("Output Depth Map")
axes[1].axis("off")
plt.show()
glb_path = hf_hub_download(
repo_id="prithivMLmods/Female-Face-Depth-3D",
filename=glb_name,
repo_type="dataset",
)
print("Downloaded mesh to:", glb_path)
```
---
## Downloading the 3D Mesh
The `glb_3d_file` field stores the filename of the corresponding 3D reconstruction. The mesh can be downloaded directly from the dataset repository using `hf_hub_download`.
```python
from huggingface_hub import hf_hub_download
mesh_path = hf_hub_download(
repo_id="prithivMLmods/Female-Face-Depth-3D",
filename=sample["glb_3d_file"],
repo_type="dataset",
)
print(mesh_path)
```
The downloaded GLB file can be viewed or processed using software such as Blender, MeshLab, Three.js, Unity, Unreal Engine, or any application that supports the GLB format.
## Dataset Features
- High-quality female face RGB images
- Dense facial depth maps
- One-to-one correspondence between RGB images and depth maps
- A corresponding GLB mesh for every sample
- Optimized Parquet dataset format for fast loading
- Compatible with the Hugging Face Datasets library
- Suitable for training, evaluation, and benchmarking of image-to-3D and face depth estimation models
- Ready for large-scale computer vision and multimodal AI pipelines
## License
This dataset is released under the **Apache-2.0 License**. |