EpiGRAF: Rethinking training of 3D GANs
Authors: Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang, Peter Wonka
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The resulting model, named Epi GRAF, is an efficient, high-resolution, pure 3D generator, and we test it on four datasets (two introduced in this work) at 2562 and 5122 resolutions. It obtains state-of-the-art image quality, high-fidelity geometry and trains 2.5 faster than the upsampler-based counterparts. |
| Researcher Affiliation | Academia | Ivan Skorokhodov Sergey Tulyakov Peter Wonka (...) 7 Acknowledgements We would like to acknowledge support from the SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence. |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code/data/visualizations: https://universome.github.io/epigraf |
| Open Datasets | Yes | We employ it for high-resolution 3D-aware image synthesis on four datasets: FFHQ [25], Cats [77], Megascans Plants, and Megascans Food. The last two benchmarks are introduced in our work and contain 360 renderings of photo-realistic scans of different plants and food objects (described in 4). They are much more complex in terms of geometry and are well-suited for assessing the structural limitations of modern 3D-aware generators. (...) Those benchmarks and the rendering code will be made publicly available. |
| Dataset Splits | No | The paper does not specify exact percentages or sample counts for training, validation, and test splits. It mentions training data but not explicit splits for reproduction. |
| Hardware Specification | Yes | Metrics. We use FID [20] to measure image quality and estimate the training cost for each method in terms of NVidia V100 GPU days needed to complete the training process. |
| Software Dependencies | No | The paper mentions software like Style GAN2, Adam, and Py Vista, but does not specify their version numbers to ensure reproducibility of the experimental setup. |
| Experiment Setup | Yes | We inherit the training procedure from Style GAN2-ADA [24] with minimal changes. The optimization is performed by Adam [27] with a learning rate of 0.002 and betas of 0 and 0.99 for both G and D. We use β(T) = 0.8 for T = 10000, z N(0, I) and set Rp = 512. D is trained with R1 regularization [37] with γ = 0.05. We train with the overall batch size of 64 for 15M images seen by D for 2562 resolution and 20M for 5122. |