DeforM: Reasoning-Guided Physics-Aware Video Generation via Spatial-Temporal Masking

• URL: http://arxiv.org/abs/2607.18664v1
• Date: Tue, 21 Jul 2026 03:23:52 GMT
• Title: DeforM: Reasoning-Guided Physics-Aware Video Generation via Spatial-Temporal Masking
• Abstract summary: Video generation models achieve high visual quality but often struggle to generate physics-aware videos.<n>We propose DeforM, a reasoning-guided image-to-video generation framework that directs the model's focus toward physics-critical regions.<n>For physical guidance, we develop two alternative strategies: DeforM-Free for training-free mechanism analysis and DeforM-Injection as a powerful training-based generator.
• Score: 46.68389408723078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be described by explicit trajectories or formulas, complex deformation dynamics remain challenging to synthesize. We observe that a lack of physical reasoning for localizing dynamic areas allows irrelevant regions to dilute the model's attention, leading to generation failure. In this paper, we propose DeforM, a reasoning-guided image-to-video generation framework that directs the model's focus toward physics-critical regions. To reason about and localize these critical regions, we introduce a VLM-guided physical reasoning module, DeforM-Reason, to identify target objects and generate spatial-temporal masks. For physical guidance, we develop two alternative strategies: DeforM-Free for training-free mechanism analysis and DeforM-Injection as a powerful training-based generator. Experimental results demonstrate that DeforM improves the realism of generated deformation scenarios, outperforming baseline models in both visual quality and physical consistency.

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