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arXiv · 2609.38615

Exo2EgoHOI: Hand-Object-Interaction Aware Exocentric-to-Egocentric Video Generation

Hongjia Zhai·Xiyu Zhang·Haoran Zhang·Zhichao Ye·Haomin Liu·Guofeng Zhang·Ian Reid·Xingxing Zuo
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Abstract

Egocentric videos of human manipulation provide valuable visual experience for embodied intelligence, yet collecting such data at scale is costly. Exocentric-to-egocentric video generation offers a scalable alternative by transforming abundant third-person manipulation videos into first-person observations. However, existing methods often struggle to faithfully preserve demonstrated hand-object interactions (HOI) across large viewpoint changes due to insufficient fine-grained interaction guidance and weak object-centric anchoring. We present Exo2EgoHOI, an HOI-aware video generative framework for interaction-preserving exocentric-to-egocentric translation. To preserve fine-grained HOI, we introduce a unified 4D HOI prior that combines scene geometry, articulated hand renderings, and dense hand-object relation fields, together with a dual-branch residual adapter for injecting structural and relational cues into the video generation backbone. To preserve object consistency, we introduce Decomposed Gated Cross-Attention, which separately encodes object and background references and adaptively integrates global semantic and local appearance features as object-centric anchors. Experiments on ARCTIC-HOI and Ego-Exo4D demonstrate substantial improvements in object consistency and HOI preservation while maintaining competitive visual fidelity. In particular, on ARCTIC-HOI, Exo2EgoHOI improves object mIoU by 32.3% and reduces MPJPE and PA-MPJPE by 34.7% and 50.0%, respectively, relative to the respective best baseline results. Project page: https://rcl-robotics.github.io/Exo2EgoHOI/.

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Hongjia Zhai, Xiyu Zhang, Haoran Zhang, Zhichao Ye, Haomin Liu, Guofeng Zhang, Ian Reid, Xingxing Zuo. 2026-09-29. Exo2EgoHOI: Hand-Object-Interaction Aware Exocentric-to-Egocentric Video Generation. https://arxiv.org/abs/2609.38615

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