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Dataset

Rgb D Segmentegocentricbodies

by ExtendedRealityLab hf-dataset--extendedrealitylab--rgb-d-segmentegocentricbodies
Nexus Index
32.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 54
R: Recency 52
Q: Quality 30
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 32.5 FNI Score
- Size
- Rows
Parquet Format
- Tokens
Dataset Information Summary
Entity Passport
Registry ID hf-dataset--extendedrealitylab--rgb-d-segmentegocentricbodies
Provider huggingface
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Cite this dataset

Academic & Research Attribution

BibTeX
@misc{hf_dataset__extendedrealitylab__rgb_d_segmentegocentricbodies,
  author = {ExtendedRealityLab},
  title = {Rgb D Segmentegocentricbodies Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/extendedrealitylab/rgb-d-segmentegocentricbodies}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
ExtendedRealityLab. (2026). Rgb D Segmentegocentricbodies [Dataset]. Free2AITools. https://huggingface.co/datasets/extendedrealitylab/rgb-d-segmentegocentricbodies

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Nexus Index V2.0

32.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 54
Recency (R) 52
Quality (Q) 30

πŸ’¬ Index Insight

FNI V2.0 for Rgb D Segmentegocentricbodies: Semantic (S:50), Authority (A:0), Popularity (P:54), Recency (R:52), Quality (Q:30).

Free2AITools Nexus Index

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Dataset Specification

text
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- odc-by
multilinguality:
- monolingual
pretty_name: 'RGB-D-SegmentEgocentricBodies '
size_categories:
- 1K

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 egocentric vision, 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

text
RGB-D-SegmentEgocentricBodies/
β”‚
β”œβ”€β”€ train/ # ~3.11 GB
β”‚ β”œβ”€β”€ images/ # RGB frames
β”‚ β”œβ”€β”€ depths/ # Real depth maps
β”‚ β”œβ”€β”€ masks/ # Segmentation masks
β”‚ └── synthetic_depths/ # Synthetic or enhanced depth maps
β”‚
β”œβ”€β”€ val/ # ~401 MB
β”‚ β”œβ”€β”€ images/
β”‚ β”œβ”€β”€ depths/
β”‚ β”œβ”€β”€ masks/
β”‚ └── synthetic_depths/
β”‚
└── .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

Acknowledgements

This dataset was created by Nokia 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} }

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


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Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-dataset--extendedrealitylab--rgb-d-segmentegocentricbodies
slug
extendedrealitylab--rgb-d-segmentegocentricbodies
source
huggingface
author
ExtendedRealityLab
license
tags
modality:image, arxiv:2207.01296, region:us

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

downloads
45,685
stars
0
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