README.md

metadata

dataset_info:
  features:
    - name: uid
      dtype: string
    - name: video_id
      dtype: string
    - name: start_second
      dtype: float32
    - name: end_second
      dtype: float32
    - name: caption
      dtype: string
    - name: fx
      dtype: float32
    - name: fy
      dtype: float32
    - name: cx
      dtype: float32
    - name: cy
      dtype: float32
    - name: vid_w
      dtype: int32
    - name: vid_h
      dtype: int32
    - name: annotation
      list:
        - name: mano_params
          struct:
            - name: global_orient
              list: float32
            - name: hand_pose
              list: float32
            - name: betas
              list: float32
        - name: is_right
          dtype: bool
        - name: keypoints_3d
          list: float32
        - name: keypoints_2d
          list: float32
        - name: vertices
          list: float32
        - name: box_center
          list: float32
        - name: box_size
          dtype: float32
        - name: camera_t
          list: float32
        - name: focal_length
          list: float32
  splits:
    - name: train
      num_examples: 241912
    - name: test
      num_examples: 5108
configs:
  - config_name: default
    data_files:
      - split: train
        path: EgoHaFL_train.csv
      - split: test
        path: EgoHaFL_test.csv
license: mit
language:
  - en
pretty_name: EgoHaFL:Egocentric 3D Hand Forecasting Dataset with Language Instruction
size_categories:
  - 200K<n<300K
tags:
  - embodied-ai
  - robotic
  - egocentric
  - 3d-hand
  - forecasting
  - hand-pose

EgoHaFL: Egocentric 3D Hand Forecasting Dataset with Language Instruction

EgoHaFL is a dataset designed for egocentric (first-person) 3D hand forecasting with accompanying natural language instructions. It contains short video clips, text descriptions, camera intrinsics, and detailed MANO-based 3D hand annotations. The dataset supports research in 3D hand forecasting, hand pose estimation, hand–object interaction understanding, and video–language modeling.

---

📦 Dataset Contents

1. Metadata CSV Files

• EgoHaFL_train.csv
• EgoHaFL_test.csv

Each row corresponds to one sample and contains:

Field	Description
uid	Unique sample identifier
video_id	Source video identifier
start_second	Start time of the clip (seconds)
end_second	End time of the clip (seconds)
caption	Natural language instruction / description
fx, fy	Camera focal lengths
cx, cy	Principal point
vid_w, vid_h	Original video resolution

---

2. 3D Hand Annotations (EgoHaFL_lmdb)

The folder EgoHaFL_lmdb stores all 3D annotations in LMDB format.

• Key: uid
• Value: a list of length 16, representing uniformly sampled frames across a 3-second video segment.

Each of the 16 elements is a dictionary containing:

• mano_params

  • global_orient (n, 1, 3 ,3)
  • hand_pose (n, 15, 3, 3)
  • betas (n, 10)

• is_right (n,)

• keypoints_3d (n, 21, 3)

• keypoints_2d (n, 21, 2)

• vertices (n, 778, 3)

• box_center (n, 2)

• box_size (n,)

• camera_t (n, 3) 3D hand position in camera coordinate

• focal_length (n, 2)

Here, n denotes the number of hands present in each frame, which may vary across frames. When no hands are detected, the dictionary is empty.

---

🌳 Annotation Structure (Tree View)

Below is the hierarchical structure for a single annotation entry (uid → 16-frame list → per-frame dict):

<uid>
└── list (length = 16)
    ├── [0]
    │   ├── mano_params
    │   │   ├── global_orient
    │   │   ├── hand_pose
    │   │   └── betas
    │   ├── is_right
    │   ├── keypoints_3d
    │   ├── keypoints_2d
    │   ├── vertices
    │   ├── box_center
    │   ├── box_size
    │   ├── camera_t
    │   └── focal_length
    ├── [1]
    │   └── ...
    ├── [2]
    │   └── ...
    └── ...

---

🎥 Source of Video Data

The video clips used in EgoHaFL originate from the Ego4D V1 dataset. For our experiments, we use the original-length videos compressed to 224p resolution to ensure efficient storage and training.

Official Ego4D website: 🔗 https://ego4d-data.org/

---

🧩 Example of Use

For details on how to load and use the EgoHaFL dataset, please refer to the dataloader implementation in our GitHub repository:

🔗 https://github.com/ut-vision/SFHand

---

🧠 Supported Research Tasks

• Egocentric 3D hand forecasting
• Hand motion prediction and trajectory modeling
• 3D hand pose estimation
• Hand–object interaction understanding
• Video–language multimodal modeling
• Temporal reasoning with 3D human hands

---

📚 Citation

If you use this dataset or find it helpful in your research, please cite:

@article{liu2025sfhand,
  title={SFHand: A Streaming Framework for Language-guided 3D Hand Forecasting and Embodied Manipulation},
  author={Liu, Ruicong and Huang, Yifei and Ouyang, Liangyang and Kang, Caixin and and Sato, Yoichi},
  journal={arXiv preprint arXiv:2511.18127},
  year={2025}
}