EgoForge: Goal-Directed Egocentric World Simulator
Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint changes, frequent hand-object interactions, and goal-directed procedures whose evolution depends on latent human intent. Existing approaches either focus on hand-centric instructional synthesis with limited scene evolution, perform static view translation without modeling action dynamics, or rely on dense supervision, such as camera trajectories, long video prefixes, synchronized multicamera capture, etc. In this work, we introduce EgoForge, an egocentric goal-directed world simulator that generates coherent, first-person video rollouts from minimal static inputs: a single egocentric image, a high-level instruction, and an optional auxiliary exocentric view. To improve intent alignment and temporal consistency, we propose VideoDiffusionNFT, a trajectory-level reward-guided refinement that optimizes goal completion, temporal causality, scene consistency, and perceptual fidelity during diffusion sampling. Extensive experiments show EgoForge achieves consistent gains in semantic alignment, geometric stability, and motion fidelity over strong baselines, and robust performance in real-world smart-glasses experiments.
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Yifan Shen
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2026-03-23T09:16:02.662Z
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Yifan Shen
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SivanSX
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Jiateng Liu
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Xinzhuo Li
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2026-03-23T09:16:00.163Z
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Xinzhuo Li
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Yuanzhe Liu
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Bingxuan Li
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Houze Yang
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Wenqi Jia
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Yijiang Li
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Tianjiao Yu
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James Matthew Rehg
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Xu Cao
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Ismini Lourentzou
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2026-04-13T07:38:06.479Z
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Ismini Lourentzou
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isminoula
EgoForge: Goal-Directed Egocentric World Simulator
Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint changes, frequent hand-object interactions, and goal-directed procedures whose evolution depends on latent human intent. Existing approaches either focus on hand-centric instructional synthesis with limited scene evolution, perform static view translation without modeling action dynamics, or rely on dense supervision, such as camera trajectories, long video prefixes, synchronized multicamera capture, etc. In this work, we introduce EgoForge, an egocentric goal-directed world simulator that generates coherent, first-person video rollouts from minimal static inputs: a single egocentric image, a high-level instruction, and an optional auxiliary exocentric view. To improve intent alignment and temporal consistency, we propose VideoDiffusionNFT, a trajectory-level reward-guided refinement that optimizes goal completion, temporal causality, scene consistency, and perceptual fidelity during diffusion sampling. Extensive experiments show EgoForge achieves consistent gains in semantic alignment, geometric stability, and motion fidelity over strong baselines, and robust performance in real-world smart-glasses experiments.
EgoForge is an egocentric goal-directed world simulator that generates coherent first-person video rollouts from minimal static inputs using trajectory-level reward-guided refinement during diffusion sampling.
generative world models egocentric video viewpoint changes hand-object interactions latent human intent instructional synthesis static view translation action dynamics dense supervision camera trajectories video prefixes multicamera capture EgoForge VideoDiffusionNFT trajectory-level reward-guided refinement diffusion sampling semantic alignment geometric stability motion fidelity