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 Squeezing-Averse Virtual Try-On via Sequential Deformation
Authors: Sang-Heon Shim, Jiwoo Chung, Jae-Pil Heo
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our SDVITON successfully resolves both types of artifacts and outperforms the baseline methods. |
| Researcher Affiliation | Academia | Sang-Heon Shim, Jiwoo Chung, Jae-Pil Heo* Sungkyunkwan University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code will be available at https://github.com/SHShim0513/SD-VITON. |
| Open Datasets | Yes | We conduct experiments on a high-resolution virtual try-on dataset introduced by VITON-HD (Choi et al. 2021). It contains 11, 647 image pairs for the training phase and 2, 032 pairs for the evaluation, each of which has a frontview woman and a top clothes with 1024 768 resolution. |
| Dataset Splits | No | The paper states '11, 647 image pairs for the training phase and 2, 032 pairs for the evaluation', but does not explicitly mention a separate validation set or specific proportions for all three splits (train/validation/test). |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies (e.g., Python, PyTorch, CUDA versions) required for reproducibility. |
| Experiment Setup | Yes | LTOCG = λCELCE + λL1(LL1 + LM L1 ) +(LVGG + LM VGG) + Lc GAN + λTVLTV + Lz-dist, (13) where λCE, λL1, λTV is set to 10, 10, and 2 for a balance. |