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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Instance-Aware Coherent Video Style Transfer for Chinese Ink Wash Painting
Authors: Hao Liang, Shuai Yang, Wenjing Wang, Jiaying Liu
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results For training, we use Chinese ink wash paintings in Chip Phi dataset [He et al., 2018], and further collect 115 videos of horses from the Internet, about 10k frames in total. PWCNet [Niklaus, 2019; Sun et al., 2018] is used to estimate optical flows for training and testing. More experimental details and results are provided in the supplementary material. |
| Researcher Affiliation | Academia | Wangxuan Institute of Computer Technology, Peking University EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper states: "Our project website is available at https://oblivioussy.github.io/Ink Video/." This is a project website, not an explicit direct link to a source code repository for the methodology. |
| Open Datasets | Yes | For training, we use Chinese ink wash paintings in Chip Phi dataset [He et al., 2018], and further collect 115 videos of horses from the Internet, about 10k frames in total. |
| Dataset Splits | No | The paper mentions using a dataset for training but does not provide specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'PWCNet' and cites its reimplementation using 'PyTorch' in the reference section, but it does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | No | The paper states 'More experimental details and results are provided in the supplementary material', but does not include specific hyperparameter values or detailed training configurations within the main text. |