Semantic-Aware Generation of Multi-View Portrait Drawings
Authors: Biao Ma, Fei Gao, Chang Jiang, Nannan Wang, Gang Xu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that SAGE achieves significantly superior or highly competitive performance, compared to existing 3Daware image synthesis methods. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, Hangzhou Dianzi University 2 Hangzhou Institute of Technology, Xidian University 3 ISN State Key Laboratory, Xidian University {aiartma, jc233, gxu}@hdu.edu.cn, {fgao, nnwang}@xidian.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available at https://github.com/Ai Art-HDU/SAGE. |
| Open Datasets | Yes | Pen-drawings. We conduct experiments on the APDrawing [Yi et al., 2019] dataset... Pencil-drawings. We apply our method to the three styles of pencil-drawings on the FS2K [Fan et al., 2022] dataset. ... Oil paintings. We randomly select 3,133 oil-paintings of humans from Wiki Art [Nichol, 2016]... Facial Photos. We use the Celeb AMask-HQ [Lee et al., 2020] dataset during training stage-I and for data augmentation. |
| Dataset Splits | No | The paper discusses training and testing datasets but does not explicitly provide details about a separate validation split, percentages, or sample counts for validation data. |
| Hardware Specification | Yes | We use a single Ge Force RTX3090 to train our model. |
| Software Dependencies | No | The paper mentions software components and optimizers (e.g., Adam optimizer, Ne RF) but does not provide specific version numbers for any libraries, frameworks, or other software dependencies. |
| Experiment Setup | Yes | In the pre-training phase of the model, we set λ1 = 0.1, λ2 = 1, and λ3 = 0.25. In the second phase of training, the parameters remain unchanged. We use the Adam optimizer and set β1 = 0 and β2 = 0.9. At 642 resolution, the learning rate of generator is 6 10 5, the learning rate of discriminators 2 10 4, and batch size 36. At 1282 resolution, the learning rate of generator is 5 10 5 and batch size 24. At 2562 resolution, the learning rate of generator is 3 10 5, the learning rate of discriminators 1 10 4, and batch size 24. |