HL-OutPaint: Coarse-to-Fine Video Outpainting for High-Resolution Long-Range Videos
Jeongeun Park, Janghyeok Han, Geonung Kim, Hyun-Seung Lee, Kyuha Choi, Youngseok Han, and Sunghyun Cho

TL;DR
HL-OutPaint is a novel high-resolution video outpainting framework that employs a coarse-to-fine approach with a global guidance mechanism to ensure spatial and temporal consistency in long, wide-range videos.
Contribution
It introduces a global-local frame swapping mechanism for constructing global coarse guidance, enabling stable, coherent outpainting of long videos with large spatial extrapolation.
Findings
Outperforms existing methods in wide spatial extrapolation tasks.
Achieves stable and coherent generation for long video sequences.
Effectively encodes long-term structure and short-term dynamics.
Abstract
Video outpainting generates plausible visual content beyond the original spatial extent of a video, playing a key role in adapting videos to diverse display formats. To support such use cases, it must enable large spatial extrapolation over long sequences. However, most existing methods address only one of these challenges or lack explicit mechanisms for ensuring global spatio-temporal consistency, leading to notable limitations. In this paper, we propose HL-OutPaint, a high-resolution video outpainting framework for long sequences. Our approach follows a coarse-to-fine strategy with a two-stage pipeline. We first construct Global Coarse Guidance (GCG), a low-resolution representation that captures global structure and dominant motion across the video. Unlike naive downsampling, GCG is built via a novel global-local frame swapping mechanism that couples sparse global keyframes with…
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