# Sketch-Guided Scenery Image Outpainting

arXiv: https://arxiv.org/abs/2006.09788 · HTML: https://mlsanity.com/paper/2006.09788/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology](https://mlsanity.com/paper/2306.13384/) (2023)
- [Hierarchical Masked 3D Diffusion Model for Video Outpainting](https://mlsanity.com/paper/2309.02119/) (2023)
- [Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach](https://mlsanity.com/paper/2401.15652/) (2024)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [Cylin-Painting: Seamless 360textdegree Panoramic Image Outpainting and Beyond](https://mlsanity.com/paper/2204.08563/) (2022)
- [CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation](https://mlsanity.com/paper/2107.13516/) (2021)
- [SemIE: Semantically-aware Image Extrapolation](https://mlsanity.com/paper/2108.13702/) (2021)
- [ReGO: Reference-Guided Outpainting for Scenery Image](https://mlsanity.com/paper/2106.10601/) (2021)
- [OutDreamer: Video Outpainting with a Diffusion Transformer](https://mlsanity.com/paper/2506.22298/) (2025)
- [Towards Reliable Image Outpainting: Learning Structure-Aware Multimodal Fusion with Depth Guidance](https://mlsanity.com/paper/2204.05543/) (2022)
- [Structure-guided Image Outpainting](https://mlsanity.com/paper/2212.12326/) (2022)
- [High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control](https://mlsanity.com/paper/2607.06162/) (2026)

## Abstract (arXiv)

The outpainting results produced by existing approaches are often too random to meet users' requirement. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network to conduct sketch-guided outpainting, where two alignment modules are adopted to impose the generated content to be realistic and consistent with the provided sketches. First, we apply a holistic alignment module to make the synthesized part be similar to the real one from the global view. Second, we reversely produce the sketches from the synthesized part and encourage them be consistent with the ground-truth ones using a sketch alignment module. In this way, the learned generator will be imposed to pay more attention to fine details and be sensitive to the guiding sketches. To our knowledge, this work is the first attempt to explore the challenging yet meaningful conditional scenery image outpainting. We conduct extensive experiments on two collected benchmarks to qualitatively and quantitatively validate the effectiveness of our approach compared with the other state-of-the-art generative models.