Generative Adversarial Networks (GANs) have achieved remarkable success in image generation. Although GAN-based approaches have also advanced three-dimensional (3D) data synthesis, they exhibit stagnation when compared to other state-of-the-art 3D generative models. Current 3D GAN methods suffer from training efficiency, generation diversity, and generalization in their original architectures. Among those challenges, cross-category training and generation are especially important in causing the degradation of synthesized results. In this paper, we propose a novel 3D generation framework to explore the capability boundaries of 3D GANs. The method features a novel style-based mechanism for controlling shape generation, a corresponding training procedure, and a CLIP-guided joint optimization scheme. This approach effectively mitigates generation diversity issues while maintaining generation quality and training stability.

The 3D scene editing method based on neural implicit field has gained wide attention. It has achieved excellent results in 3D editing tasks. However, existing methods often blend the interaction between objects and scene environment. The change of scene appearance like shadows is failed to be displayed in the rendering view. In this paper, we propose an Object and Scene environment Interaction aware (OSI-aware) system, which is a novel two-stream neural rendering system considering object and scene environment interaction. To obtain illuminating conditions from the mixture soup, the system successfully separates the interaction between objects and scene environment by intrinsic decomposition method. To study the corresponding changes to the scene appearance from object-level editing tasks, we introduce a depth map guided scene inpainting method and shadow rendering method by point matching strategy. Extensive experiments demonstrate that our novel pipeline produce reasonable appearance changes in scene editing tasks. It also achieve competitive performance for the rendering quality in novel-view synthesis tasks.