GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats
Authors: Sangeek Hyun, Jae-Pil Heo
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that ours achieves a significantly faster rendering speed ( 100) compared to state-of-the-art 3D consistent GANs with comparable 3D generation capability. |
| Researcher Affiliation | Academia | Sangeek Hyun Sungkyunkwan University Jae-Pil Heo Sungkyunkwan University |
| Pseudocode | No | The paper describes its methods using text and figures but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | First of all, please refer to the attached code in supplementary for a more detailed implementation of the proposed method. |
| Open Datasets | Yes | Following the experimental settings of previous 3D GANs [12, 25], we use FFHQ [35] and AFHQ-Cat [50] datasets with 256 256 and 512 512 resolutions. |
| Dataset Splits | No | The paper states using FFHQ [35] and AFHQ-Cat [50] datasets and follows experimental settings of previous 3D GANs [12, 25], but it does not explicitly provide specific training/validation/test dataset split percentages or sample counts within its text. |
| Hardware Specification | Yes | Rendering time is measured on a single RTX A6000 GPU. ... Additionally, the training time of the proposed methods is 28 RTX A6000 days, while the state-of-the-art Mimic3D requires 64 A100 days on the FFHQ-512 dataset. |
| Software Dependencies | No | The paper mentions various models and techniques like StyleGAN2 and Neus2 but does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For the coefficients of objective functions, we use λ = 1, λpose = 1, and λknn = 10. We train the model until the discriminator sees 10-15M images. ... For the architectural details, we use an upsampling ratio r = 4 and the initial number of Gaussians N = 256. For the number of hierarchical levels, we adopt L = 5 for the dataset with 256 256 resolution, and L = 6 for 512 512 resolution. |