Create README.md

README.md

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+annotations_creators:
+- other
+language:
+- en
+language_creators:
+- other
+license:
+- odc-by
+multilinguality:
+- monolingual
+pretty_name: 'RGB-D-SegmentEgocentricBodies '
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- egocentric segmentation
+- extended reality
+- xr
+- human-body
+- mixed-reality
+- avatar
+task_categories:
+- image-segmentation
+- depth-estimation
+task_ids:
+- semantic-segmentation
+-  features:
+    - name: image
+      dtype: image
+    - name: depth
+      dtype: image
+    - name: mask
+      dtype: image
+    - name: synthetic_depth
+      dtype: image
+-splits:
+    - name: train
+      num_examples: 8005
+    - name: validation
+      num_examples: 1069
+
+# RGB-D Segment Egocentric Bodies Dataset
+
+## Overview
+
+The **RGB-D Segment Egocentric Bodies Dataset** is a multi-modal dataset designed for **egocentric body segmentation and depth-aware perception**. It contains synchronized **RGB images**, **real depth maps**, **segmentation masks**, and **synthetic depth data**, captured from an egocentric point of view.  
+The dataset is intended to support research in **XR/VR/AR**, **human–computer interaction**, and **depth-aware computer vision**.
+
+## Dataset Description
+
+The dataset is an extension of the EgoBodies Dataset (please refer to https://arxiv.org/pdf/2207.01296 for more information), with depth frames. We provide two versions of depth: real depth images acquired with different sensors: 
+RealSense D435, Realsense L515. Synthetic detph were estimated using Depth-Anything by Yang et al (2024). It is composed of more than 40 different users, in wild scenarios. 
+
+## Dataset Structure
+
+RGB-D-SegmentEgocentricBodies/
+│
+├── train/ # ~3.11 GB
+│ ├── images/ # RGB frames
+│ ├── depths/ # Real depth maps
+│ ├── masks/ # Segmentation masks
+│ └── synthetic_depths/ # Synthetic or enhanced depth maps
+│
+├── val/ # ~401 MB
+│ ├── images/
+│ ├── depths/
+│ ├── masks/
+│ └── synthetic_depths/
+│
+└── .gitattributes # Git LFS configuration
+
+## Intended Use
+
+This dataset is suitable for:
+
+- Egocentric human / body-part segmentation  
+- Depth-aware perception models  
+- XR avatar embodiment and telepresence  
+- Mixed-reality interaction research  
+- Training and benchmarking RGB-D models
+
+
+## Example Usage
+
+```python
+from PIL import Image
+import numpy as np
+import os
+
+def load_sample(root, split, idx):
+    base = os.path.join(root, split)
+    rgb = Image.open(os.path.join(base, "images", f"{idx}.png"))
+    depth = Image.open(os.path.join(base, "depths", f"{idx}.png"))
+    mask = Image.open(os.path.join(base, "masks", f"{idx}.png"))
+    synth = Image.open(os.path.join(base, "synthetic_depths", f"{idx}.png"))
+    return rgb, depth, mask, synth
+
+## Acknowledgements
+
+This dataset was created by ExtendedRealityLab and developed in the context of research on egocentric perception and immersive telepresence.
+If you use this dataset in academic work, please cite the following papers:
+
+@article{gonzalez2023full,
+  title={Full body video-based self-avatars for mixed reality: from e2e system to user study},
+  author={Gonzalez Morin, Diego and Gonzalez-Sosa, Ester and Perez, Pablo and Villegas, Alvaro},
+  journal={Virtual Reality},
+  volume={27},
+  number={3},
+  pages={2129--2147},
+  year={2023},
+  publisher={Springer}
+}
+
+@article{gonzalez2022real,
+  title={Real time egocentric segmentation for video-self avatar in mixed reality},
+  author={Gonzalez-Sosa, Ester and Gajic, Andrija and Gonzalez-Morin, Diego and Robledo, Guillermo and Perez, Pablo and Villegas, Alvaro},
+  journal={arXiv preprint arXiv:2207.01296},
+  year={2022}
+}
+
+@article{tobaruela2026egocentricrgbd,
+  title={RGB-D Egocentric Segmentation of Human Bodies for XR Applications},
+  author={Pedros-Tobaruela, Sofia and Gonzalez-Sosa, Ester and Perez, Pablo and Villegas, Alvaro},
+  journal={submitted}
+}
+
+