NP-Hand: Novel Perspective Hand Image Synthesis Guided by Normals

Synthesizing multi-view images that are geometrically consistent with a given single-view image is one of the hot issues in AIGC in recent years. Existing methods have achieved impressive performance on objects with symmetry or rigidity, but they are inappropriate for the human hand. Because an image-captured human hand has more diverse poses and less attractive textures. In this paper, we propose NP-Hand, a framework that elegantly combines the diffusion model and generative adversarial network: The multi-step diffusion is trained to synthesize low-resolution novel perspective, while the single-step generator is exploited to further enhance synthesis quality. To maintain the consistency between inputs and synthesis, we creatively introduce normal maps into NP-Hand to guide the whole synthesizing process. Comprehensive evaluations have demonstrated that the proposed framework is superior to existing state-of-the-art models and more suitable for synthesizing hand images with faithful structures and realistic appearance details. The code will be released on our website.