---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: bbox
    list: float64
  - name: focal_length
    dtype: float64
  - name: pred_keypoints_3d
    list:
      list: float64
  - name: pred_keypoints_2d
    list:
      list: float64
  - name: pred_vertices
    list:
      list: float64
  - name: pred_cam_t
    list: float64
  - name: pred_pose_raw
    list: float64
  - name: global_rot
    list: float64
  - name: body_pose_params
    list: float64
  - name: hand_pose_params
    list: float64
  - name: scale_params
    list: float64
  - name: shape_params
    list: float64
  - name: expr_params
    list: float64
  - name: mask
    dtype: 'null'
  - name: pred_joint_coords
    list:
      list: float64
  - name: pred_global_rots
    list:
      list:
        list: float64
  - name: lhand_bbox
    list: float64
  - name: rhand_bbox
    list: float64
  - name: height
    dtype: float64
  - name: weight
    dtype: float64
  - name: gender
    dtype: int64
  - name: age
    dtype: int64
  splits:
  - name: train
    num_bytes: 2968667338
    num_examples: 5499
  - name: test
    num_bytes: 330391774
    num_examples: 612
  download_size: 2100652040
  dataset_size: 3299059112
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
  - image-segmentation
  - image-classification
  - image-feature-extraction
  - feature-extraction
  - keypoint-detection
  - zero-shot-object-detection
tags:
  - keypoint-detection
  - human-mesh-recovery
  - 3d-pose-estimation
  - sam-3d-body
  - mhr
  - biometrics
  - human-body
  - pose-estimation
  - 3d-pose
  - age-estimation
  - height-estimation
  - weight-estimation
  - gender-classification
  - celebrity
base_dataset:
  - alecccdd/celeb-fbi
pretty_name: Celeb-FBI Pose Estimation
size_categories:
- 1K<n<10K
---

# Celeb-FBI Pose Estimation Dataset

A derivative of the [Celeb-FBI dataset](https://huggingface.co/datasets/alecccdd/celeb-fbi) enriched with 3D human pose and mesh recovery annotations generated using Meta's [SAM 3D Body](https://github.com/facebookresearch/sam-3d-body) model.

## Dataset Description

This dataset extends the original Celeb-FBI celebrity full-body image dataset with comprehensive 3D pose estimation outputs. Each sample includes predicted 3D keypoints, 2D keypoints, mesh vertices, body/hand pose parameters, and shape parameters extracted using the `facebook/sam-3d-body-vith` model.

The dataset retains the original biometric annotations (height, weight, age, gender) from Celeb-FBI, enabling joint research on pose estimation and human attribute prediction.

### Dataset Structure

```python
DatasetDict({
    train: Dataset({
        features: ['id', 'bbox', 'focal_length', 'pred_keypoints_3d', 'pred_keypoints_2d', 
                   'pred_vertices', 'pred_cam_t', 'pred_pose_raw', 'global_rot', 
                   'body_pose_params', 'hand_pose_params', 'scale_params', 'shape_params', 
                   'expr_params', 'mask', 'pred_joint_coords', 'pred_global_rots', 
                   'lhand_bbox', 'rhand_bbox', 'height', 'weight', 'gender', 'age'],
        num_rows: 5499
    })
    test: Dataset({
        features: [...],
        num_rows: 612
    })
})
```

### Features

#### Pose Estimation Outputs (SAM 3D Body)

| Feature | Type | Description |
|---------|------|-------------|
| `bbox` | sequence[float] | Detected person bounding box |
| `focal_length` | float | Estimated camera focal length |
| `pred_keypoints_3d` | sequence[sequence[float]] | Predicted 3D keypoint coordinates |
| `pred_keypoints_2d` | sequence[sequence[float]] | Predicted 2D keypoint coordinates |
| `pred_vertices` | sequence[sequence[float]] | Predicted mesh vertices (MHR format) |
| `pred_cam_t` | sequence[float] | Predicted camera translation |
| `pred_pose_raw` | sequence[float] | Raw pose parameters |
| `global_rot` | sequence[float] | Global rotation parameters |
| `body_pose_params` | sequence[float] | Body pose parameters |
| `hand_pose_params` | sequence[float] | Hand pose parameters |
| `scale_params` | sequence[float] | Scale parameters |
| `shape_params` | sequence[float] | Body shape parameters |
| `expr_params` | sequence[float] | Expression parameters |
| `mask` | Image | Segmentation mask |
| `pred_joint_coords` | sequence[sequence[float]] | Predicted joint coordinates |
| `pred_global_rots` | sequence[sequence[float]] | Predicted global rotations per joint |
| `lhand_bbox` | sequence[float] | Left hand bounding box |
| `rhand_bbox` | sequence[float] | Right hand bounding box |

#### Biometric Annotations (from Celeb-FBI)

| Feature | Type | Description |
|---------|------|-------------|
| `id` | string | Unique identifier (matches original Celeb-FBI) |
| `height` | float | Height in centimeters (-1 if missing/invalid) |
| `weight` | float | Weight in kilograms (-1 if missing/invalid) |
| `gender` | int | 0 = Male, 1 = Female |
| `age` | int | Age in years (-1 if missing/invalid) |

### Sample Count

| Split | Original Celeb-FBI | This Dataset | Notes |
|-------|-------------------|--------------|-------|
| Train | 6,487 | 5,499 | Single-person images only |
| Test | 721 | 612 | Single-person images only |
| **Total** | **7,208** | **6,111** | ~85% retained |

**Note:** Images where the SAM 3D Body model detected ≠1 person have been excluded to ensure clean, unambiguous pose annotations.

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("alecccdd/celeb-fbi-pose-estimation")

# Access training data
train_data = dataset["train"]

# Example: iterate over samples
for sample in train_data:
    # Pose estimation outputs
    keypoints_3d = sample["pred_keypoints_3d"]
    keypoints_2d = sample["pred_keypoints_2d"]
    body_pose = sample["body_pose_params"]
    shape = sample["shape_params"]
    
    # Original biometric data
    height = sample["height"]  # in cm, -1 if missing
    weight = sample["weight"]  # in kg, -1 if missing
    gender = sample["gender"]  # 0=male, 1=female
    age = sample["age"]        # -1 if missing

# Filter samples with valid height annotations
valid_height = train_data.filter(lambda x: x["height"] != -1)

# Get samples with both pose and complete biometric data
complete_samples = train_data.filter(
    lambda x: x["height"] != -1 and x["weight"] != -1 and x["age"] != -1
)
```

### Accessing Original Images

The original images are available in the base dataset:

```python
from datasets import load_dataset

# Load both datasets
pose_data = load_dataset("alecccdd/celeb-fbi-pose-estimation")
image_data = load_dataset("alecccdd/celeb-fbi")

# Create a lookup by ID
image_lookup = {sample["id"]: sample["image"] for sample in image_data["train"]}

# Access image for a pose sample
pose_sample = pose_data["train"][0]
original_image = image_lookup.get(pose_sample["id"])
```

## Processing Pipeline

This dataset was created by:

1. Loading all images from `alecccdd/celeb-fbi`
2. Running inference with `facebook/sam-3d-body-vith` on each image
3. Filtering to retain only images where exactly one person was detected
4. Extracting and storing all pose estimation outputs alongside original annotations

## Intended Uses

- **Pose-conditioned attribute estimation**: Using 3D pose features to improve height, weight, or age prediction
- **Multi-task learning**: Joint pose estimation and biometric prediction
- **Pose analysis**: Studying pose distributions in celebrity photography
- **Benchmarking**: Evaluating pose estimation models on standing, front-facing poses
- **Shape-from-pose research**: Investigating relationships between body shape parameters and physical attributes

## Limitations

- **Single-person constraint**: Multi-person images have been removed
- **Celebrity bias**: Images may not represent general population poses and body types
- **Pseudo-ground-truth**: Pose annotations are model predictions, not manual annotations
- **Standing poses**: Original dataset focuses on front-facing, standing positions
- **Inherited noise**: Some biometric annotations from Celeb-FBI contain errors

## Ethical Considerations

- This dataset uses publicly available images of celebrities
- Pose estimation can be used for surveillance; users should consider privacy implications
- Body shape parameters may encode sensitive attributes
- Please use responsibly and in accordance with applicable laws

## Citation

If you use this dataset, please cite both the SAM 3D Body model and the original Celeb-FBI paper:

```bibtex
@article{yang2025sam3dbody,
  title={SAM 3D Body: Robust Full-Body Human Mesh Recovery},
  author={Yang, Xitong and Kukreja, Devansh and Pinkus, Don and Sagar, Anushka and Fan, Taosha and Park, Jinhyung and Shin, Soyong and Cao, Jinkun and Liu, Jiawei and Ugrinovic, Nicolas and Feiszli, Matt and Malik, Jitendra and Dollar, Piotr and Kitani, Kris},
  journal={arXiv preprint},
  year={2025}
}

@misc{debnath2024celebfbibenchmarkdatasethuman,
  title={Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach}, 
  author={Pronay Debnath and Usafa Akther Rifa and Busra Kamal Rafa and Ali Haider Talukder Akib and Md. Aminur Rahman},
  year={2024},
  eprint={2407.03486},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2407.03486}, 
}
```

## Related Resources

- **Base Dataset**: [alecccdd/celeb-fbi](https://huggingface.co/datasets/alecccdd/celeb-fbi)
- **SAM 3D Body Model**: [facebook/sam-3d-body-vith](https://huggingface.co/facebook/sam-3d-body-vith)
- **SAM 3D Body Code**: [github.com/facebookresearch/sam-3d-body](https://github.com/facebookresearch/sam-3d-body)
- **SAM 3D Body Paper**: [arXiv](https://arxiv.org/abs/2407.03486)
- **Original Celeb-FBI Paper**: [arXiv:2407.03486](https://arxiv.org/abs/2407.03486)