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.

Global illumination combines direct and indirect lighting to create realistic lighting effects, bringing virtual scenes closer to reality. Static global illumination is a crucial component of virtual scene rendering, leveraging precomputation and baking techniques to significantly reduce runtime computational costs. Unfortunately, many existing works prioritize visual quality by relying on extensive texture storage and massive pixel-level texture sampling, leading to large performance overhead. In this paper, we introduce an illumination reconstruction method that effectively reduces sampling in fragment shader and avoids additional render passes, making it well-suited for low-end platforms. To achieve high-quality global illumination with reduced memory usage, we adopt a spherical harmonics fitting approach for baking effective illumination information and propose an inverse probe distribution method that generates unique probe associations for each mesh. This association, which can be generated offline in the local space, ensures consistent lighting quality across all instances of the same mesh. As a consequence, our method delivers highly competitive lighting effects while using only approximately 5% of the memory required by mainstream industry techniques.

The 3D Gaussian Splatting (3D-GS) method has recently sparked a revolution in novel view synthesis with its remarkable visual effects and fast rendering speed. However, its reliance on simple spherical harmonics for color representation leads to subpar performance in complex scenes, particularly with effects like specular highlights and light refraction. Also, 3D-GS adopts a periodic split strategy, which significantly increases the model's disk space and hinders rendering efficiency. To tackle these challenges, we propose Gaussian Splatting with Neural Basis Extension (GSNB), a novel approach that substantially enhances the performance of 3D-GS in demanding scenes while reducing storage consumption. Drawing inspiration from basis function, GSNB utilizes a light-weight MLP to share feature coefficients with Spherical Harmonics (SH). This extends the color calculation of 3D Gaussians, resulting in more accurate visual effect modeling. This combination allows GSNB to achieve remarkable results even in scenes with challenging lighting and reflection conditions. Additionally, GSNB uses pre-computation to bake the MLP's output, thereby alleviating inference workload and subsequent speed loss. Furthermore, to leverage the capabilities of Neural Basis Extension and eliminate redundant Gaussians, we propose a new importance criterion to prune the converged Gaussian model and obtain a more compact representation through re-optimization. Our experimental results demonstrate that our method delivers high-quality rendering in most scenarios and effectively reduces redundant Gaussians without compromising rendering speed. The code is available at https://github.com/Dojizz/GSNB/.