TensorGRAF: Tensorial Generative Radiance Field

3D-aware generative methods based on neural radiance fields are gaining attention. Nevertheless, they suffer from slow training and execution speeds due to volume rendering and deep neural networks. We propose using a voxel grid as the explicit representation of the radiance field, combining a shallow network to interpret the spatial features. We employ tensor decomposition to convert the voxel into axis-aligned feature vectors, reducing synthesis space complexity from O(n³) to O(n). Additionally, we leverage the well-established 2D generative adversarial network structure in our 1D feature vector generator.