# EgoMe: A New Dataset and Challenge for Following Me via Egocentric View in Real World

arXiv: https://arxiv.org/abs/2501.19061 · HTML: https://mlsanity.com/paper/2501.19061/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 47 of its 64 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 30 of 47)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering](https://mlsanity.com/paper/1704.04497/) (2017)
- [Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities](https://mlsanity.com/paper/2203.14712/) (2022)
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [H2O: Two Hands Manipulating Objects for First Person Interaction Recognition](https://mlsanity.com/paper/2104.11181/) (2021)
- [Modeling Context Between Objects for Referring Expression Understanding](https://mlsanity.com/paper/1608.00525/) (2016)
- [A Joint Sequence Fusion Model for Video Question Answering and Retrieval](https://mlsanity.com/paper/1808.02559/) (2018)
- [HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World](https://mlsanity.com/paper/2309.17024/) (2023)
- [End-to-End Dense Video Captioning with Parallel Decoding](https://mlsanity.com/paper/2108.07781/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Charades-Ego: A Large-Scale Dataset of Paired Third and First Person Videos](https://mlsanity.com/paper/1804.09626/) (2018)
- [Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild](https://mlsanity.com/paper/2406.09905/) (2024)
- [Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos](https://mlsanity.com/paper/2204.01696/) (2022)
- [EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World](https://mlsanity.com/paper/2403.16182/) (2024)
- [CLIP4Caption: CLIP for Video Caption](https://mlsanity.com/paper/2110.06615/) (2021)
- [My View is the Best View: Procedure Learning from Egocentric Videos](https://mlsanity.com/paper/2207.10883/) (2022)
- [The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain](https://mlsanity.com/paper/2010.05654/) (2020)
- [TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks](https://mlsanity.com/paper/2011.11479/) (2020)
- [HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training](https://mlsanity.com/paper/2212.14546/) (2022)
- [HierVL: Learning Hierarchical Video-Language Embeddings](https://mlsanity.com/paper/2301.02311/) (2023)
- [Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning](https://mlsanity.com/paper/2210.06031/) (2022)
- [Home Action Genome: Cooperative Compositional Action Understanding](https://mlsanity.com/paper/2105.05226/) (2021)

## Built on by (most cited first, 8 of 8)

- [Challenges and Trends in Egocentric Vision: A Survey](https://mlsanity.com/paper/2503.15275/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2605.18431/) (2026)
- [Optimizing Multimodal LLMs for Egocentric Video Understanding: A Solution for the HD-EPIC VQA Challenge](https://mlsanity.com/paper/2601.10228/) (2026)
- [EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks](https://mlsanity.com/paper/2604.09535/) (2026)
- [RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination](https://mlsanity.com/paper/2607.14187/) (2026)
- [CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation](https://mlsanity.com/paper/2604.09201/) (2026)
- [CoMind: Understanding Collaborative Human Activity from Multiple Minds and Views](https://mlsanity.com/paper/2607.06691/) (2026)

## Abstract (arXiv)

In human imitation learning, the imitator typically take the egocentric view as a benchmark, naturally transferring behaviors observed from an exocentric view to their owns, which provides inspiration for researching how robots can more effectively imitate human behavior. However, current research primarily focuses on the basic alignment issues of ego-exo data from different cameras, rather than collecting data from the imitator's perspective, which is inconsistent with the high-level cognitive process. To advance this research, we introduce a novel large-scale egocentric dataset, called EgoMe, which towards following the process of human imitation learning via the imitator's egocentric view in the real world. Our dataset includes 7902 paired exo-ego videos (totaling15804 videos) spanning diverse daily behaviors in various real-world scenarios. For each video pair, one video captures an exocentric view of the imitator observing the demonstrator's actions, while the other captures an egocentric view of the imitator subsequently following those actions. Notably, EgoMe uniquely incorporates exo-ego eye gaze, other multi-modal sensor IMU data and different-level annotations for assisting in establishing correlations between observing and imitating process. We further provide a suit of challenging benchmarks for fully leveraging this data resource and promoting the robot imitation learning research. Extensive analysis demonstrates significant advantages over existing datasets. Our EgoMe dataset and benchmarks are available at https://huggingface.co/datasets/HeqianQiu/EgoMe.