EasyOutPainter: One Step Image Outpainting With Both Continuous Multiple and Resolution.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Image outpainting, the generation of content beyond an image's boundaries, is a challenging task for current generative models.
• Existing methods typically support discrete expansion multiples and fixed resolutions, limiting flexibility.

Purpose of the Study:

• To develop image outpainting methods capable of continuous multiples and arbitrary resolutions.
• To achieve single-step outpainting for any expansion multiple and resolution.
• To eliminate the reliance on pre-trained backbone networks.

Main Methods:

• Arbitrary multiple outpainting is achieved using randomly cropped views and relative positional embeddings during training.
• Continuous-resolution outpainting is enabled by a multi-scale training strategy that disentangles resolution and patch count.
• A query-based contrastive objective is proposed to avoid dependence on pre-trained models.

Main Results:

• The proposed method successfully generates outpainted images with continuous expansion multiples.
• Arbitrary resolutions can be achieved without postprocessing.
• Experimental results demonstrate superior performance compared to state-of-the-art approaches on public benchmarks.

Conclusions:

• The developed image outpainting techniques offer unprecedented flexibility in terms of expansion multiples and resolutions.
• The single-step approach simplifies the outpainting process.
• This work advances the capabilities of generative models for image manipulation and content creation.