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..
Texture Reformer: Towards Fast and Universal Interactive Texture Transfer
Authors: Zhizhong Wang, Lei Zhao, Haibo Chen, Ailin Li, Zhiwen Zuo, Wei Xing, Dongming Lu2624-2632
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on a variety of application scenarios demonstrate the effectiveness and superiority of our framework. And compared with the state-of-the-art interactive texture transfer algorithms, it not only achieves higher quality results but, more remarkably, also is 2-5 orders of magnitude faster. We apply our framework to many challenging interactive texture transfer tasks, and demonstrate its effectiveness and superiority through extensive comparisons with the state-of-the-art (SOTA) algorithms. In Table 1, we compare the running time with the competitors. |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Zhejiang University EMAIL |
| Pseudocode | No | The paper describes procedures using numbered steps within paragraphs (e.g., in Section 3.1 for SGTW), but it does not present these as formal pseudocode blocks or clearly labeled algorithm sections. |
| Open Source Code | Yes | Code is available at https://github.com/EndyWon/Texture-Reformer. |
| Open Datasets | Yes | The decoders are trained on the Microsoft COCO dataset (Lin et al. 2014) |
| Dataset Splits | No | The paper mentions training on the Microsoft COCO dataset but does not specify any dataset splits (e.g., percentages or counts for training, validation, or testing data) needed for reproduction. |
| Hardware Specification | Yes | 1 Tested on a 3.3 GHz hexa-core CPU and a 6GB Nvidia 1060 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages beyond the general mentions in the text. |
| Experiment Setup | Yes | The hyperparameters that control the semantic-awareness (Eq. 4) in stage I and stage II are set to ω1 = ω2 = 50 (ω1 for stage I, ω2 for stage II. See supplementary material (SM) for their effects). |