Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals

• URL: http://arxiv.org/abs/2601.05848v1
• Date: Fri, 09 Jan 2026 15:23:36 GMT
• Title: Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals
• Authors: Nate Gillman, Yinghua Zhou, Zitian Tang, Evan Luo, Arjan Chakravarthy, Daksh Aggarwal, Michael Freeman, Charles Herrmann, Chen Sun,
• Abstract summary: Goal Force allows users to define goals via explicit force vectors and intermediate dynamics.<n>We train a video generation model on a curated dataset of synthetic causal primitives.<n>Our results suggest that by grounding video generation in fundamental physical interactions, models can emerge as implicit neural physics simulators.
• Score: 15.286299359279509
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in video generation have enabled the development of ``world models'' capable of simulating potential futures for robotics and planning. However, specifying precise goals for these models remains a challenge; text instructions are often too abstract to capture physical nuances, while target images are frequently infeasible to specify for dynamic tasks. To address this, we introduce Goal Force, a novel framework that allows users to define goals via explicit force vectors and intermediate dynamics, mirroring how humans conceptualize physical tasks. We train a video generation model on a curated dataset of synthetic causal primitives-such as elastic collisions and falling dominos-teaching it to propagate forces through time and space. Despite being trained on simple physics data, our model exhibits remarkable zero-shot generalization to complex, real-world scenarios, including tool manipulation and multi-object causal chains. Our results suggest that by grounding video generation in fundamental physical interactions, models can emerge as implicit neural physics simulators, enabling precise, physics-aware planning without reliance on external engines. We release all datasets, code, model weights, and interactive video demos at our project page.

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