Instance-Aware Coherent Video Style Transfer for Chinese Ink Wash Painting
Authors: Hao Liang, Shuai Yang, Wenjing Wang, Jiaying Liu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {lianghao17, williamyang, daooshee, liujiaying}@pku.edu.cn |
| 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. |