VideoCanvas: Unified Video Completion from Arbitrary Spatiotemporal Patches via In-Context Conditioning

Existing controllable video generation methods are typically designed for rigid, task-specific settings, such as first-frame image-to-video, inpainting, or interpolation, treating spatio-temporal control as a set of isolated problems. We formalize a unified task, arbitrary spatio-temporal video completion, where a model generates a coherent video from user-specified patches placed at any spatial location and timestamp. However, realizing such a unified framework within modern latent video diffusion models is non-trivial: causal video VAEs compress multiple frames into a single latent slot, making frame-level conditioning fundamentally ill-posed, and directly feeding sparsely populated, zero-padded video inputs into the VAE leads to severe out-of-distribution artifacts. To address these challenges, we propose VideoCanvas, a simple yet effective framework that adapts the In-Context Conditioning paradigm to arbitrary spatio-temporal completion without modifying or retraining the VAE. Our key idea is a hybrid conditioning strategy that decouples spatial and temporal control: spatially, we encode zero-padded full-frame canvases in image mode to keep VAE inputs in-distribution, and temporally we use Temporal RoPE Interpolation to assign each condition a continuous fractional index in the latent sequence for precise frame-level alignment. To evaluate this capability, we develop VideoCanvasBench, the first benchmark for arbitrary spatio-temporal video completion, covering both intra-scene fidelity and inter-scene creativity. Extensive experiments demonstrate that VideoCanvas achieves state-of-the-art performance across a diverse range of video generation tasks under a single, unified framework.