Physically Plausible Video Generation via Visual-Semantic Chain-of-Events Conditioning

• URL: http://arxiv.org/abs/2609.00656v1
• Date: Tue, 01 Sep 2026 03:35:02 GMT
• Title: Physically Plausible Video Generation via Visual-Semantic Chain-of-Events Conditioning
• Abstract summary: We reformulate PPVG as event-centric generation by representing physical evolution as a chain of causally connected and physically constrained events.<n>Our framework generates videos with superior physical plausibility across diverse domains.
• Score: 48.21630532401797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physically Plausible Video Generation (PPVG) seeks to synthesize videos consistent with physical principles, yet remains challenging due to underspecified natural language conditioning. Advanced chain-of-thought (CoT) frameworks augment prompts with physical knowledge. However, such prompts describe physical phenomena holistically, overlooking intermediate states and transition dynamics. In this paper, we reformulate PPVG as event-centric generation by representing physical evolution as a chain of causally connected and physically constrained events. Our framework comprises three key modules: (1) Physics-driven Event Chain Reasoning. This module decomposes physical phenomena into causally connected events represented by evolving scene graphs. Formula-derived physical quantities are bound to relevant objects and interactions, characterizing the direction and magnitude of each event transition. (2) Transition-aware Routed Keyframe Conditioning. This module routes each event to a specialized keyframe synthesis operator for appearance variation or object transformation. Consecutive keyframes are injected as residual guidance during denoising, enabling smooth visual transitions between event-boundary states. (3) Physics-injected Contrastive Semantic Guidance. This module constructs physics-informed positive and counterfactual negative prompts for classifier-free guidance, steering generation toward plausible dynamics and away from physics-violating counterparts. Experiments on PhyGenBench, VideoPhy, PhyWorldBench, and Physics-IQ demonstrate that our framework generates videos with superior physical plausibility across diverse domains.

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