Code Video Model

Video diffusion models generate realistic videos in a black box. Physics is imagined rather than computed; object placement, motion, and camera trajectories are resolved internally and only visible in the final clip. Inspired by film production, we present Code Video Model, which gives video generation an executable previs, with code as the engine. Given a prompt, Code Video Model writes a program that lays out a scene, animates objects, places a camera, and computes dynamics from equations; rendering the program yields a proxy video and a reference image. Rendering the resulting proxy video takes seconds, making it fast to preview; it is grounded, since dynamics are computed, and directly and precisely controllable, because code can modify each element. A pretrained video model is then conditioned on the proxy video and reference image to generate the final clip. However, we observe that existing video models tend to copy the flat appearance of the proxy video into the generated clip. To address this issue, Code Video Model further introduces early-step conditioning, a training-free schedule that attends to the proxy video only during early sampling steps when structure is established, and drops this conditioning thereafter. As a result, Code Video Model is able to generate a final clip that follows the proxy video’s structure while preserving the reference image’s appearance. We introduce CodeVideoBench to evaluate controllable video generation across diverse applications including gaming, scene editing, world-model data, physics grounding, bullet time, and 3D/4D reconstruction. On CodeVideoBench, Code Video Model with MiniMax-H3 achieves overall scores of 85.2, 82.1, and 85.8 under QwenVL-3.8-27B, GPT-5.6, and GPT-6, respectively, versus 74.3, 73.1, and 80.6 for VACE, the strongest learned baseline. The project page is available at https://anonymous.4open.science/w/CodeVideoModel-2027.