Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
One-Shot Reference-based Structure-Aware Image to Sketch Synthesis
Authors: Rui Yang, Honghong Yang, Li Zhao, Qin Lei, Mianxiong Dong, Kaoru Ota, Xiaojun Wu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model demonstrates superior performance across multiple evaluation metrics, including user style preference. Extensive experiments conducted on various standard benchmarks demonstrate our superiority in terms of sketch quality, flexibility, and applicability. |
| Researcher Affiliation | Academia | 1 Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, School of Computer Science, Shaanxi Normal University, Xi an, 710119, China 2College of Computer Science, Chongqing University, Chongqing, 400044, China 3Muroran Institute of Technology, Muroran, Hokkaido, 0508585, Japan |
| Pseudocode | No | The paper only describes the methodology in prose and mathematical formulas, without explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/Ref2Sketch-SA |
| Open Datasets | Yes | Datasets: Four datasets were used for evaluation: 4SKST (Seo, Ashtari, and Noh 2023), FS2K (Fan et al. 2022), Anime (Kang 2018), and APDrawings (Yi et al. 2019). |
| Dataset Splits | No | The paper mentions several datasets (4SKST, FS2K, Anime, APDrawings) used for evaluation but does not specify how these datasets were split into training, validation, or test sets. |
| Hardware Specification | Yes | All experiments were conducted on a single NVIDIA 3090 GPU |
| Software Dependencies | No | The paper mentions using Stable Diffusion XL (SDXL) and IP-Adapter, but does not provide specific version numbers for these or other software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Specifically, the sampling result zt 1 in each denoising operation is altered by ˆzt 1: ˆzt 1 = zt 1 ζ zt Lacu, (11) where ζ is set to 3. |