Semantic-Aware 3D GAN: CLIP-Guided Disentanglement for Efficient Cross-Category Shape Generation
Weinan Cai, Zongji Wang, Yuanben Zhang, Zhihong Zeng, Xinming Li, Junyi Liu · Applied Sciences · 2025
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.