# EgoExoBench: A Benchmark for First- and Third-person View Video Understanding in MLLMs

arXiv: https://arxiv.org/abs/2507.18342 · HTML: https://mlsanity.com/paper/2507.18342/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 86 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 30 of 59)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [LVBench: An Extreme Long Video Understanding Benchmark](https://mlsanity.com/paper/2406.08035/) (2024)
- [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022)
- [HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction](https://mlsanity.com/paper/2203.01577/) (2022)
- [Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities](https://mlsanity.com/paper/2203.14712/) (2022)
- [H2O: Two Hands Manipulating Objects for First Person Interaction Recognition](https://mlsanity.com/paper/2104.11181/) (2021)
- [Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions](https://mlsanity.com/paper/2111.10337/) (2021)
- [NVILA: Efficient Frontier Visual Language Models](https://mlsanity.com/paper/2412.04468/) (2024)
- [iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks](https://mlsanity.com/paper/2108.03272/) (2021)

## Built on by (most cited first, 23 of 23)

- [MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence](https://mlsanity.com/paper/2512.10863/) (2025)
- [Holistic Evaluation of Multimodal LLMs on Spatial Intelligence](https://mlsanity.com/paper/2508.13142/) (2025)
- [EgoThinker: Unveiling Egocentric Reasoning with Spatio-Temporal CoT](https://mlsanity.com/paper/2510.23569/) (2025)
- [Exo2EgoSyn: Unlocking Foundation Video Generation Models for Exocentric-to-Egocentric Video Synthesis](https://mlsanity.com/paper/2511.20186/) (2025)
- [Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline](https://mlsanity.com/paper/2603.05484/) (2026)
- [EgoExo-Con: Exploring View-Invariant Video Temporal Understanding](https://mlsanity.com/paper/2510.26113/) (2025)
- [Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos](https://mlsanity.com/paper/2607.11523/) (2026)
- [EgoVITA: Learning to Plan and Verify for Egocentric Video Reasoning](https://mlsanity.com/paper/2511.18242/) (2025)
- [Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2605.18431/) (2026)
- [Thinking in Structures: Evaluating Spatial Intelligence in Constraint-Governed Spaces](https://mlsanity.com/paper/2602.07864/) (2026)
- [SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting](https://mlsanity.com/paper/2511.18127/) (2025)
- [From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation](https://mlsanity.com/paper/2604.13793/) (2026)
- [CrossView: Can Vision-Language Models Reason Across Cameras?](https://mlsanity.com/paper/2608.15539/) (2026)
- [Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents](https://mlsanity.com/paper/2511.18685/) (2025)
- [The N-Body Problem: Parallel Execution from Single-Person Egocentric Video](https://mlsanity.com/paper/2512.11393/) (2025)
- [DenseStep2M: A Scalable, Training-Free Pipeline for Dense Instructional Video Annotation](https://mlsanity.com/paper/2604.26565/) (2026)
- [EgoCoT-Bench: Benchmarking Grounded and Verifiable Operation-Centric Chain of Thought Reasoning for MLLMs](https://mlsanity.com/paper/2605.19559/) (2026)
- [VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis](https://mlsanity.com/paper/2605.22570/) (2026)
- [CaST-Bench: Benchmarking Causal Chain-Grounded Spatio-Temporal Reasoning for Video Question Answering](https://mlsanity.com/paper/2605.23216/) (2026)
- [Spatial-IQ: Deconstructing Spatial Intelligence via Hierarchical Capability Tests](https://mlsanity.com/paper/2607.22864/) (2026)
- [A Survey of Large Models in Sports](https://mlsanity.com/paper/2608.14377/) (2026)
- [Ego-Forge: Text and Geometric-Attention Free Exo-to-Egocentric Video Generation](https://mlsanity.com/paper/2609.35368/) (2026)
- [EgoExo-Next:Benchmarking Vision-Language Models on Visual-Option Next-State and Cross-View Reasoning](https://mlsanity.com/paper/2610.04506/) (2026)

## Abstract (arXiv)

Transferring and integrating knowledge across first-person (egocentric) and third-person (exocentric) viewpoints is intrinsic to human intelligence, enabling humans to learn from others and convey insights from their own experiences. Despite rapid progress in multimodal large language models (MLLMs), their ability to perform such cross-view reasoning remains unexplored. To address this, we introduce EgoExoBench, the first benchmark for egocentric-exocentric video understanding and reasoning. Built from publicly available datasets, EgoExoBench comprises over 7,300 question-answer pairs spanning eleven sub-tasks organized into three core challenges: semantic alignment, viewpoint association, and temporal reasoning. We evaluate 13 state-of-the-art MLLMs and find that while these models excel on single-view tasks, they struggle to align semantics across perspectives, accurately associate views, and infer temporal dynamics in the ego-exo context. We hope EgoExoBench can serve as a valuable resource for research on embodied agents and intelligent assistants seeking human-like cross-view intelligence.