HL-OutPaint enables high-resolution outpainting of long video sequences via a coarse-to-fine pipeline that first builds Global Coarse Guidance through global-local frame swapping then synthesizes details.
In: Proceedings of the 33rd ACM International Conference on Multimedia
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 3years
2026 3roles
background 1polarities
background 1representative citing papers
A two-stage diffusion framework generates a layout-controllable low-resolution blueprint to guide parallel high-resolution artwork outpainting, achieving 2.4× speedup and improved fidelity over sequential baselines.
Kernel interpolation with a constant multiplier scales convolution and fully-connected layers in neural networks to higher resolutions or dimensions without training, producing competitive results on Stable Diffusion and other models.
citing papers explorer
-
HL-OutPaint: Coarse-to-Fine Video Outpainting for High-Resolution Long-Range Videos
HL-OutPaint enables high-resolution outpainting of long video sequences via a coarse-to-fine pipeline that first builds Global Coarse Guidance through global-local frame swapping then synthesizes details.
-
High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control
A two-stage diffusion framework generates a layout-controllable low-resolution blueprint to guide parallel high-resolution artwork outpainting, achieving 2.4× speedup and improved fidelity over sequential baselines.
-
Supersampling Stable Diffusion and Beyond: A Seamless, Training-Free Approach for Scaling Neural Networks Using Common Interpolation Methods
Kernel interpolation with a constant multiplier scales convolution and fully-connected layers in neural networks to higher resolutions or dimensions without training, producing competitive results on Stable Diffusion and other models.