Hierarchical Diffusion for Camera-controlled Autoregressive Video Generation

We address the task of long-video, camera-controlled generation from a single image. The dominant autoregressive paradigm suffers from two key limitations: (1) error accumulation over time, as distant frames are conditioned on previously generated outputs rather than ground truth, leading to drift; and (2) ambiguity in how to incorporate camera control. Standard solutions—such as self-forcing and conditioning on explicit 6-DOF poses—either incur significant computational overhead or produce inconsistent scene scale in generated videos. We propose a simple and lightweight framework that combines two models, WanAR and FixAR, built on online reconstruction and hierarchical denoising. To enforce consistency with the input camera scale, we draw from feed-forward novel-view synthesis and reformulate video generation as a warping-and-inpainting problem. To mitigate drift, we introduce a hierarchical denoising strategy in which different stages condition on different temporal contexts. Specifically, we first generate frames conditioned on the immediate past, and then refine them using a second denoiser conditioned on a temporally subsampled long-range history to enforce global consistency. This fine-to-coarse scheme enables each generated frame to effectively condition on the full temporal context without incurring a prohibitive cost. Experiments on RealEstate10k and DL3DV demonstrate that WanAR+FixAR outperforms prior methods for camera-controlled long-video generation, producing results that are both temporally stable and consistent with the scale of the input camera trajectories.