Hao Li, Xiyuan Zheng, Yuanyuan Gao, Yalun Dai, Xinhao Ji, Xuan-Li Chen, Jing Zhao, Fangzhou Hong · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26549 · open-access PDF
人間の自己中心的な経験は、世界がどのように組織され、どのように相互作用を通じて変化するかを学ぶための豊富な基盤を提供します。この潜在力を規模で実現するには、シーン構造、オブジェクトセマンティクス、および相互作用の歴史を視点と時間を通じて接続する学習リソースに録画の増大する量を変える必要がある一方で、高価な手動注記への依存を減らす。
Machine translation (Meta M2M100); the English original follows.
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
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