Domain Transfer Generative Model for New Face Generation

We propose a novel fast approach for face image generation based on a domain transfer method. This approach incorporates variance injection within each gradual transfer process, which is facilitated by a conditioned U-Net and a novel pattern similarity loss. The variance injection enables the exploration of unseen regions within each domain, resulting in the generation of diverse, photorealistic images that extend beyond the training data distribution. Our approach offers a promising alternative to GAN-based and diffusion models, providing enhanced controllability on the generation process. This work reveals the potential of domain transfer as a new type of generative model, though further research is needed to fully explore and refine this nascent framework. Results of extensive experimental work have shown that this new fast approach can successfully generate quality photorealistic faces with diverse face features. It verifies that this is a new and attractive approach for new data synthesis with high-quality.