paper-with-me

홈 › Papers

DanbooRegion: An Illustration Region Dataset

2020-08-01 · ECCV 2020 8 · Lvmin Zhang, Yi JI, Chunping Liu

Region is a fundamental element of various cartoon animation techniques and artistic painting applications. Achieving satisfactory region is essential to the success of these techniques. Motivated to assist diversiform region-based cartoon applications, we invite artists to annotate regions for in-the-wild cartoon images with several application-oriented goals: (1) To assist image-based cartoon rendering, relighting, and cartoon intrinsic decomposition literature, artists identify object outlines and eliminate lighting-and-shadow boundaries. (2) To assist cartoon inking tools, cartoon structure extraction applications, and cartoon texture processing techniques, artists clean-up texture or deformation patterns and emphasize cartoon structural boundary lines. (3) To assist region-based cartoon digitalization, clip-art vectorization, and animation tracking applications, artists inpaint and reconstruct broken or blurred regions in cartoon images. Given the typicality of these involved applications, this dataset is also likely to be used in other cartoon techniques. We detail the challenges in achieving this dataset and present a human-in-the-loop workflow namely Feasibility-based Assignment Recommendation (FAR) to enable large-scale annotating. The FAR tends to reduce artist trails-and-errors and encourage their enthusiasm during annotating. Finally, we present a dataset that contains a large number of artistic region compositions paired with corresponding cartoon illustrations. We also invite multiple professional artists to assure the quality of each annotation.

📄 PDF Abstract BibTeX

Code (1)

lllyasviel/DanbooRegion 공식 구현 tf

Similar Papers 제목 키워드 기반

LLMs Behind the Scenes: Enabling Narrative Scene Illustration

2025-09-26 · Melissa Roemmele, John Joon Young Chung, Taewook Kim, Yuqian Sun 외 arxiv

Generative AI has established the opportunity to readily transform content from one medium to another. This capability is especially powerful for storytelling, where visual illustrations can illuminate a story originally…

CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation

2025-12-03 · Ruoxuan Zhang, Bin Wen, Hongxia Xie, Yi Yao 외 arxiv

Cooking is a sequential and visually grounded activity, where each step such as chopping, mixing, or frying carries both procedural logic and visual semantics. While recent diffusion models have shown strong capabilities…

Text-to-Image Generation

Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations

2022-05-28 · Matheus K. Venturelli, Pedro H. Gomes, Jônatas Wehrmann

In this paper, we propose MAGICSTYLEGAN and MAGICSTYLEGAN-ADA - both incarnations of the state-of-the-art models StyleGan2 and StyleGan2 ADA - to experiment with their capacity of transfer learning into a rather differen…

Transfer Learning

PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters

2023-03-25 · CVPR 2023 1 · Shuhong Chen, Kevin Zhang, Yichun Shi, Heng Wang 외

We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challenges to single-view reconstruction; compar…

3D Architecture3D ReconstructionSingle-View 3D Reconstruction

AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations

2026-02-03 · Minjun Zhu, Zhen Lin, Yixuan Weng, Panzhong Lu 외 arxiv

High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottleneck in both academia and industry. We …