DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation

Long-trajectory video generation is a crucial yet challeng-ing task for world modeling, primarily due to the limited scalability ofexisting video diffusion models (VDMs). Autoregressive models, whileoffering infinite rollout, suffer from visual drift and poor controllability.To address these issues, we propose DCARL, a novel divide-and-conquer,autoregressive framework that effectively combines the structural stabil-ity of the divide-and-conquer scheme with the high-fidelity generationof VDMs. Our approach first employs a dedicated Keyframe Generatortrained without temporal compression to establish long-range, globallyconsistent structural anchors. Subsequently, an Interpolation Generatorsynthesizes the dense frames in an autoregressive manner with overlap-ping segments, utilizing the keyframes for global context and a singleclean preceding frame for local coherence. Trained on a large-scale inter-net long trajectory video dataset, our method achieves superior perfor-mance in both visual quality (lower FID and FVD) and camera adher-ence (lower ATE and ARE) compared to state-of-the-art autoregressiveand divide-and-conquer baselines, demonstrating stable and high-fidelitygeneration for long trajectory videos up to 32 seconds in length.