Update README.md

README.md

@@ -1,44 +1,164 @@
 ---
 license: cc-by-nc-4.0
-language:
-- en
-tags:
-- tactile
-- egocentric-vision
-- robotics
-size_categories:
-- 5G
+language: [en]
+pretty_name: EgoTactile
+tags: [tactile, tactile-sensing, egocentric-vision, grasp-pressure, robotics, computer-vision, video, multimodal]
 ---
 
-# Dataset for ICML 2026 Submission
+# EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
 
-**⚠️ ANONYMITY WARNING:** This dataset is part of a double-blind submission to **ICML 2026**. The repository name and contents have been anonymized. Please do not attempt to identify the authors or institution.
+This repository contains the official dataset for:
+
+> **EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video**  
+> **ICML 2026 Spotlight**
+
+- Paper: https://arxiv.org/abs/2606.09243
+- Project Page: https://egotactile.github.io/
 
 ## Dataset Summary
 
-This repository contains **EgoTactile**, a large-scale benchmark dataset pairing egocentric RGB video with synchronized full-hand pressure signals. The dataset is designed to address the challenges of estimating dynamic grasp pressure from vision under occlusion and physical ambiguity.
+**EgoTactile** is a large-scale benchmark that pairs egocentric RGB video with synchronized full-hand pressure measurements during everyday object grasping.
+
+The dataset is designed to support research on estimating dynamic grasp pressure from visual observations. This task is challenging because hand-object contact regions are frequently occluded, while visually similar grasping observations may correspond to different pressure distributions.
+
+EgoTactile includes:
+
+- 12 participants
+- 63 everyday objects
+- 7 object categories
+- Egocentric RGB video recorded at 1280 × 720 resolution and 15 FPS
+- Full-hand tactile pressure measurements from 162 sensing locations
+- Pressure measurements within a 0–350 N range
+- Object and participant metadata
+- Temporally synchronized visual and tactile streams at 15 Hz
+- A bare-hand subset for evaluating transfer to natural hand appearances
+
+## Associated Methods
+
+The accompanying paper introduces two methods evaluated on EgoTactile:
+
+- **EgoPressureFormer**, a discriminative baseline for full-hand grasp-pressure estimation from egocentric video.
+- **EgoPressureDiff**, a conditional diffusion framework that adapts a pretrained video diffusion backbone for pressure estimation under partial visual observations and physical ambiguity.
+
+Please refer to the paper for complete methodological and experimental details.
+
+## Dataset Organization
+
+The dataset consists of two primary subsets corresponding to different acquisition protocols.
+
+### 1. Gloved-Hand Set
+
+The Gloved-Hand Set contains synchronized egocentric RGB videos and tactile pressure measurements collected while participants wear the tactile sensing glove.
+
+#### Purpose
+
+This subset provides direct supervision for learning mappings from egocentric video observations to full-hand pressure distributions.
+
+#### Contents
+
+- Egocentric RGB video frames
+- Synchronized 162-dimensional pressure measurements
+- Object metadata
+- Anonymized participant metadata
+- Temporal and sequence identifiers
+
+### 2. Bare-Hand Set
+
+The Bare-Hand Set is designed to evaluate transfer to natural hand appearances without a visible tactile glove.
+
+During data acquisition, the hand visible to the egocentric camera is bare, while a synchronized off-camera gloved hand performs the corresponding grasping action and provides the tactile pressure reference. The two actions are coordinated using metronome guidance.
+
+#### Purpose
+
+This subset supports evaluation of transfer from instrumented gloved-hand observations to natural bare-hand scenarios.
+
+#### Contents
+
+- Egocentric RGB videos of bare-hand grasping
+- Synchronized tactile pressure references
+- Object metadata
+- Anonymized participant metadata
+- Temporal and sequence identifiers
+
+## Modalities
+
+### Egocentric RGB Video
+
+- Resolution: 1280 × 720
+- Frame rate: 15 FPS
+- Viewpoint: head-mounted egocentric camera
+- Content: hand-object grasping interactions
+
+### Tactile Pressure
+
+- Number of sensing locations: 162
+- Sampling rate: 15 Hz after synchronization
+- Pressure range: 0–350 N
+- Coverage: full-hand tactile sensing
+- Alignment: temporally synchronized with the RGB video stream
+
+### Object Metadata
+
+Object metadata includes:
+
+- Object name
+- Object category
+- Weight
+- Surface material
+- Fill state
+
+### Participant Metadata
+
+Participant metadata includes:
+
+- Anonymized participant ID (`p001`–`p012`)
+- Gender
+- Hand length
+
+Participant identities are not included in the released dataset.
+
+## Intended Uses
+
+EgoTactile is intended for research in areas including:
+
+- Grasp-pressure estimation
+- Vision-based tactile inference
+- Egocentric hand-object interaction understanding
+- Multimodal representation learning
+- Tactile sensing
+- Robotic manipulation
+- Human-robot interaction
+- Transfer from instrumented hands to natural bare hands
+
+## Limitations
+
+Users should consider the following limitations:
+
+- The dataset contains a finite set of participants, objects, and grasping behaviors.
+- Data were collected under controlled acquisition conditions.
+- Pressure references in the Bare-Hand Set are obtained through synchronized paired actions rather than direct sensing on the visible bare hand.
+- Performance on unseen environments, camera configurations, object types, and manipulation behaviors may differ from the reported benchmark results.
+- Participant-level attributes should not be used for identity inference or unintended profiling.
+
+## License
 
-The dataset features:
-* **Scale:** 12 participants grasping 63 everyday objects across 7 categories.
-* **Modalities:** * Egocentric RGB Video (1280x720, 15fps).
-    * Dense Tactile Pressure (162 sensors, 0-350N range).
-    * Physical Metadata (Object weight, material, subject attributes).
-* **Synchronization:** All streams are temporally aligned at 15Hz.
+EgoTactile is released under the **Creative Commons Attribution-NonCommercial 4.0 International License**, abbreviated as **CC BY-NC 4.0**.
 
-## Dataset Structure
+The dataset may be used for non-commercial research purposes with appropriate attribution. Users are responsible for complying with the license terms.
 
-The data is organized into two primary subsets corresponding to the acquisition modes described in the submission:
+## Citation
 
-### 1. Gloved-Hand Set (Supervised Learning)
-Contains paired samples where the hand visible in the video is wearing the tactile glove.
-* **Purpose:** Direct supervised learning of pressure from video.
-* **Content:** Synchronized RGB frames and ground-truth pressure sequences.
+Please cite the following paper when using EgoTactile:
 
-### 2. Bare-Hand Set (Transfer Evaluation)
-Contains weakly paired samples to transfer to natural, bare hands.
-* **Setup:** The visible hand is bare, while a synchronized off-camera gloved hand provides the pressure label (via metronome guidance).
+```bibtex
+@article{zeng2026egotactile,
+  title   = {EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video},
+  author  = {Zeng, Yuan and Shi, Yujia and Tan, Tiao and Li, Xingting and Qin, Yaqi and Lu, Zongqing and Yang, Wenming and Xue, Jing-Hao and Liao, Qingmin},
+  journal = {arXiv preprint arXiv:2606.09243},
+  year    = {2026}
+}
+```
 
-### Metadata
-* **Object Attributes:** Name, Category, Weight (g), Surface Material, Fill State.
-* **Subject Attributes:** Anonymized ID (p001-p012), Gender, Hand Length.
+## Contact
 
+For questions about the dataset, benchmark, or accompanying paper, please refer to the contact information provided on the project page or open an issue in the corresponding public repository.