Capsule networks get achievements in many computer vision tasks. However, in the field of image generation, they have huge room for improvement compared with the mainstream models. This is because capsule networks cannot fully parse useful features and have limited capabilities of modeling the hierarchical and geometrical structure of the object in background noise. To tackle these issues, we propose a capsule autoencoder that can learn the part–object spatial hierarchical features, and we dub it an adversarial capsule autoencoder with style vector (StyleACAE). Specifically, StyleACAE decomposes the object into a set of semantic-consistent part-level descriptions and then assembles them into object-level descriptions to learn the spatial hierarchy information. Furthermore, we effectively apply the modified generator structure that contains novel style modulation and demodulation, which extracts the valid information of the capsules and eliminates the scaled effect of styles. The experimental results show that StyleACAE can generate high-quality images and has competitive performance to the state-of-the-art generative models.