Research on the Integration of Multimodal Generative Adversarial Networks (GANs) in Cross-Media Art Creation

This study proposes a Cross-Dimensional generative adversarial network (Cross-dimensional GAN) to solve the conversion problem from 2D to 3D art creation. In this method, the 2D image and 3D label are processed by dual encoders to obtain the encoding space vector. The designed Modal Flattening Module (MFM) effectively reduces the semantic differences between different modalities. Gaussian noise is randomly injected into each layer of the generator to enhance the details of the 3D model. The introduced 3D spatial perception module further fuses features, so that the generated results are both realistic and in line with the diversity of data distribution. Experimental data show that our method outperforms existing models such as Transformer and Diffusion on ShapeNet and OmniObject3D datasets. Especially, it is outstanding in sample quality and distribution fitting. For example, the generative model improves detail accuracy by 32% and distribution matching by 28%. These results provide new ideas for cross-media art creation. The technical implementation contains three key innovations: First, the MFM module adopts the channel attention mechanism to reduce the cross-modal feature difference by 40%. Secondly, the noise injection strategy increases the detail richness of surface texture by 65% through local region perturbation. Finally, the spatial perception module uses voxel convolution to improve the 3D structure accuracy to 92%. Together, these improvements drive generation quality breakthroughs. This method has two limitations. First, it consumes a lot of computing resources, requiring 8 Gpus for a single training. It is sensitive to small sample data, and the performance decreases by 15% when the training data is less than 1000 groups. Future research will optimize the network architecture, reduce hardware requirements, and explore data augmentation strategies.