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..
Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning
Authors: Zaiyu Huang, Hanhui Li, Zhenyu Xie, Michael Kampffmeyer, qingling Cai, Xiaodan Liang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public benchmarks and our Hard Pose test set demonstrate the superiority of our method against the SOTA try-on approaches. 4 Experiments In this section, we conduct extensive experiments to validate the effectiveness of the proposed 3D-GCL network. |
| Researcher Affiliation | Collaboration | 1Shenzhen Campus of Sun Yat-Sen University 2Byte Dance, 3 Ui T The Arctic University of Norway 4Peng Cheng Laboratory {huangzy225, xiezhy6} @mail2.sysu.edu.cn {lihh77, caiqingl} @mail.sysu.edu.cn EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and data are provided in the supplementary material. |
| Open Datasets | Yes | Our experiments are conducted on two open-source datasets, Deep Fashion [31] and MPV [32], which contain 52,712 and 37,723 fashion images, respectively. |
| Dataset Splits | No | To ensure a fair comparison, we follow the train/test split of [20, 22, 33] on the Deep Fashion dataset and use the original split of MPV. In this way, we get 101,622/8,564 train/test pairs for Deep Fashion and 52,236/10,544 train/test pairs for MPV. The paper specifies train/test splits but does not explicitly mention a separate validation split with quantities or percentages. |
| Hardware Specification | Yes | The proposed 3D-GCL network is implemented in Py Torch and trained with 4 Tesla V100 GPUs. |
| Software Dependencies | No | The proposed 3D-GCL network is implemented in Py Torch and trained with 4 Tesla V100 GPUs. We thank Mind Spore for the partial support of this work, which is a new deep learning computing framwork5. The paper mentions PyTorch and MindSpore but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We first train the correspondence estimation subnetwork for 20 epochs with a batch-size of 8, and then follow the settings of [20, 25] to train the try-on generator. |