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Holi-World: Building Holistic World Intelligence from Human-Ego Experience

Hao Li, Xiyuan Zheng, Yuanyuan Gao, Yalun Dai, Xinhao Ji, Xuan-Li Chen, Jing Zhao, Fangzhou Hong, Zhaoxi Chen, Ziwei Liu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26549 · cited by 0 works (OpenAlex) · open-access PDF

Russian

Реализация этого потенциала в масштабе требует превращения растущих объемов записей в ресурсы обучения, которые соединяют структуру сцены, предметную семантику и историю взаимодействия через точки зрения и время, при этом уменьшая зависимость от дорогостоящего ручного обозначения. Мы вводим HOLI-WORLD, интегрированную экосистему, соединяющую историю взаимодействия, представление мира, автоматическое строительство данных, обучение моделям и независимую оценку. Чтобы поддержать обучение в масштабе, HOLIWORLD-Foundry объединяет дополнительную статическую и динамическую восприятие, чтобы автоматически построить мировые записи, которые соединяют стойкую структуру сцены и развивающиеся объекты с историей взаимодействия и поддерживающими наблюдения. HOLIWORLD-Benata организует эти записи

Machine translation (Meta M2M100); the English original follows.

Abstract

Human egocentric experience offers a rich foundation for learning how the world is organized and how it changes through interaction. Realizing this potential at scale requires turning growing volumes of recordings into learning resources that connect scene structure, object semantics, and interaction histories across viewpoints and time, while reducing dependence on costly manual annotation. We introduce HOLI-WORLD, an integrated ecosystem connecting experience capture, world representation, automated data construction, model learning, and independent evaluation. To sup- port learning at scale, HOLIWORLD-Foundry combines complementary static and dynamic perception to automatically construct world records that connect persistent scene structure and evolving object states with interaction histories and supporting observations. HOLIWORLD-Data organizes these records from approximately 100 self-collected scenes into grounded learning resources, including approximately 80K QA pairs used to train HOLIWORLD-VL-4B. Independent human annotations of heldout recordings form HOLIWORLD-Bench, supporting evaluation of both annotation quality and learned spatial and temporal understanding. Experiments on ScanNet, ScanNet++, Aria Digital Twin, and HOLIWORLD-Bench demonstrate strong performance in static perception and dynamic tracking, while training on HOLIWORLD-Data substantially improves HOLIWORLD-VL-4B's spatial and temporal reasoning on HOLI- WORLD-Bench and EgoDynamic4D. HOLIWORLD provides a practical foundation for expanding learning resources from the continuous flow of human experience and advancing machine understanding of the dynamic world.

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Topics: Computer science, Viewpoints, Artificial intelligence, Perception, Construct (python library), Object (grammar)

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