CycleVTON: A Cycle Mapping Framework for Parser-Free Virtual Try-On
Authors: Chenghu Du, Junyin Wang, Yi Rong, Shuqing Liu, Kai Liu, Shengwu Xiong
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on challenging benchmarks demonstrate that our proposed method exhibits superior performance compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070 2 Shanghai Artificial Intelligence Laboratory, Shanghai 200232 3 Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, 572000 4 School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, 430200 5 School of Information Science and Technology, Qiongtai Normal University, Haikou, 571127 |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | VITON We use VITON dataset (Han et al. 2018), which consists of 16,253 image groups with the resolution of 256 192. ... VITON-HD We also use VITON-HD dataset collected by (Choi et al. 2021) to demonstrate the generalization of handling high-resolution images, which comprises 13,679 image groups with the resolution of 512 384. |
| Dataset Splits | No | The dataset is split into a training set with 14,221 groups and a testing set with 2,032 groups. (for VITON). All components are the same as VITON, and are split into a training set with 11,647 groups and a testing set with 2,032 groups. (for VITON-HD). The paper only mentions training and testing splits, not an explicit validation split. |
| Hardware Specification | Yes | Our framework is implemented using Py Torch and trained on 1 Nvidia Tesla V100 GPU running Ubuntu 16.04. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | During training, we use the Adam W optimizer (β1 = 0.5 and β2 = 0.999) (Loshchilov and Hutter 2017) with a batch size of 1 and an initial learning rate of 1e 4. Our framework is iteratively optimized for 200 epochs, the learning rate is linearly reduced to 0 in the last 100 epochs. |