💡 The paper introduces VideoCoCo, a novel framework for physically consistent video generation. The problem addressed is that existing text-to-video models struggle to generate videos with physically consistent dynamics, as they must infer the temporal evolution of a scene implicitly from a highly compressed text prompt. Current chain-of-thought approaches introduce intermediate plans or visual states, but these representations are typically non-executable or temporally sparse, limiting their ability to instantiate and control the complete spatiotemporal process.

To address this limitation, VideoCoCo uses an agentic dual-engine system, where executable Blender code serves as a process-level chain of thought. Given a text prompt, a coding agent synthesizes a Blender program that explicitly specifies the scene and its temporal evolution. The executable simulation engine runs the program to produce a deterministic spatiotemporal draft, which is then transformed into a photorealistic video by a generative video engine through draft-conditioned editing.

The authors also construct VideoCoCo-3K, a curated dataset of draft-instruction-target triplets to adapt the video editor to simulated drafts. The results demonstrate that VideoCoCo improves the OmniWeaving baseline from 0.475 to 0.558 on PhyGenBench and from 52.18 to 77.88 on VBench-2.0, achieving the best average score on both benchmarks. The findings show that executable code provides an effective, controllable, and inspectable intermediate representation for physically consistent video generation.