Ego-centric Predictive Model Conditioned on Hand Trajectories

• URL: http://arxiv.org/abs/2508.19852v2
• Date: Thu, 28 Aug 2025 07:08:03 GMT
• Title: Ego-centric Predictive Model Conditioned on Hand Trajectories
• Authors: Binjie Zhang, Mike Zheng Shou,
• Abstract summary: In egocentric scenarios, anticipating both the next action and its visual outcome is essential for understanding human-object interactions.<n>We propose a unified two-stage predictive framework that jointly models action and visual future in egocentric scenarios.<n>Our approach is the first unified model designed to handle both egocentric human activity understanding and robotic manipulation tasks.
• Score: 52.531681772560724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In egocentric scenarios, anticipating both the next action and its visual outcome is essential for understanding human-object interactions and for enabling robotic planning. However, existing paradigms fall short of jointly modeling these aspects. Vision-Language-Action (VLA) models focus on action prediction but lack explicit modeling of how actions influence the visual scene, while video prediction models generate future frames without conditioning on specific actions, often resulting in implausible or contextually inconsistent outcomes. To bridge this gap, we propose a unified two-stage predictive framework that jointly models action and visual future in egocentric scenarios, conditioned on hand trajectories. In the first stage, we perform consecutive state modeling to process heterogeneous inputs (visual observations, language, and action history) and explicitly predict future hand trajectories. In the second stage, we introduce causal cross-attention to fuse multi-modal cues, leveraging inferred action signals to guide an image-based Latent Diffusion Model (LDM) for frame-by-frame future video generation. Our approach is the first unified model designed to handle both egocentric human activity understanding and robotic manipulation tasks, providing explicit predictions of both upcoming actions and their visual consequences. Extensive experiments on Ego4D, BridgeData, and RLBench demonstrate that our method outperforms state-of-the-art baselines in both action prediction and future video synthesis.

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