G‐SplatGAN: Disentangled 3D Gaussian Generation for Complex Shapes via Multi‐Scale Patch Discriminators

Abstract Generating 3D objects with complex topologies from monocular images remains a challenge in computer graphics, due to the difficulty of modeling varying 3D shapes with disentangled, steerable geometry and visual attributes. While NeRF‐based methods suffer from slow volumetric rendering and limited structural controllability. Recent advances in 3D Gaussian Splatting provide a more efficient alternative and its generative modeling with separate control over structure and appearance remains underexplored. In this paper, we propose G‐SplatGAN , a novel 3D‐aware generation framework that combines the rendering efficiency of 3D Gaussian Splatting with disentangled latent modeling. Starting from a shared Gaussian template, our method uses dual modulation branches to modulate geometry and appearance from independent latent codes, enabling precise shape manipulation and controllable generation. We adopt a progressive adversarial training scheme with multi‐scale and patch‐based discriminators to capture both global structure and local detail. Our model requires no 3D supervision and is trained on monocular images with known camera poses, reducing data reliance while supporting real image inversion through a geometry‐aware encoder. Experiments show that G‐SplatGAN achieves superior performance in rendering speed, controllability and image fidelity, offering a compelling solution for controllable 3D generation using Gaussian representations.