Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics
Authors: Hiromu Yakura, Yuki Koyama, Masataka Goto
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with various third-party tools, such as Instagram and Blender, show that our framework can effectively leverage deep learning techniques for computational design support. |
| Researcher Affiliation | Academia | 1University of Tsukuba, Japan 2National Institute of Advanced Industrial Science and Technology (AIST), Japan |
| Pseudocode | No | The paper describes the framework conceptually and visually through diagrams, but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using publicly available models from GitHub but does not state that the authors are releasing their own source code for the methodology presented in this paper. |
| Open Datasets | Yes | We randomly selected ten pairs of the original and reference selfies from the dataset of Gu et al. [2019] and prepared imitating selfies by using our framework. |
| Dataset Splits | No | The paper focuses on leveraging pretrained models and conducting subjective evaluations. It does not describe any specific training/validation dataset splits used for training models in this work, as the models they employ are already pretrained. |
| Hardware Specification | No | The paper mentions using an "Android emulator" for experiments but does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments or the pretrained models. |
| Software Dependencies | No | The paper mentions using "UIAutomator", "Optuna", "Instagram", "SNOW", and "Blender", but it does not provide specific version numbers for these software components or any other underlying libraries/frameworks. |
| Experiment Setup | Yes | Then we used Optuna for 1,000 iterations to find parameters of the makeup transformations, such as lip color and eyebrows, that minimize the distance to the reference selfie in the latent space. |