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..
FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on
Authors: Chenhui Wang, Tao Chen, Zhihao Chen, Zhizhong Huang, Taoran Jiang, Qi Wang, Hongming Shan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on the benchmark VITON-HD and Dress Code datasets demonstrate that our FLDM-VTON outperforms state-of-the-art baselines and is able to generate photo-realistic try-on images with faithful clothing details. |
| Researcher Affiliation | Collaboration | 1 Institute of Science and Technology for Brain-inspired Intelligence, MOE Frontiers Center for Brain Science, and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China 2 School of Computer Science, Fudan University, Shanghai 200433, China 3 Suzhou Xiangji Technology Service Co., Ltd., Suzhou 215223, China 4 Shanghai Center for Brain Science and Brain-inspired Technology, Shanghai 200031, China |
| Pseudocode | No | The paper describes its methods in text and figures but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We conduct experiments on two popular high-resolution VTON benchmarks: the VITON-HD dataset [Choi et al., 2021] and Dress Code dataset [Morelli et al., 2022]. |
| Dataset Splits | No | The paper states: 'We follow the official guidelines to divide the data into training and testing sets [Choi et al., 2021; Morelli et al., 2022].' It specifies training and testing sets but does not explicitly mention a validation set or its split details. |
| Hardware Specification | Yes | We adopt Adam optimizer to optimize all networks with a mini-batch size of 8 and a learning rate of 2.0 × 10−5 on 4 NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer', 'SD KL-regularized auto-encoder', 'DPM solver', and 'Free U', but it does not provide specific version numbers for any of these to ensure reproducibility of the software environment. |
| Experiment Setup | Yes | We adopt Adam optimizer to optimize all networks with a mini-batch size of 8 and a learning rate of 2.0 × 10−5 on 4 NVIDIA V100 GPUs. In addition, we employ the encoder and decoder of SD KL-regularized auto-encoder, with a down-sampling factor of d = 8 and a latent channel number of c = 4, as our encoder E and decoder D, respectively. We set T = 1, 000 for latent diffusion training as suggested by SD [Lee et al., 2022], and use the DPM solver [Lu et al., 2022] with 50 sampling steps for inference. |