June 6, 2024, 4:49 a.m. | Sangeek Hyun, Jae-Pil Heo

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.02968v1 Announce Type: new
Abstract: Most advances in 3D Generative Adversarial Networks (3D GANs) largely depend on ray casting-based volume rendering, which incurs demanding rendering costs. One promising alternative is rasterization-based 3D Gaussian Splatting (3D-GS), providing a much faster rendering speed and explicit 3D representation. In this paper, we exploit Gaussian as a 3D representation for 3D GANs by leveraging its efficient and explicit characteristics. However, in an adversarial framework, we observe that a na\"ive generator architecture suffers from training …

adversarial arxiv cs.cv generative hierarchical type

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