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

Area of Science:

• Computer Vision
• Artificial Intelligence
• Computer Graphics

Background:

• Synthesizing geometrically consistent multi-view images is a key challenge in AI-generated content (AIGC).
• Existing methods struggle with the diverse poses and textures of human hands.
• Human hands present unique challenges due to their complex articulation and appearance variations.

Purpose of the Study:

• To develop a novel framework for synthesizing geometrically consistent multi-view images of human hands.
• To address the limitations of current methods in generating realistic hand imagery.
• To improve the fidelity and realism of AI-generated human hand images.

Main Methods:

• Proposed NP-Hand framework combining diffusion models and generative adversarial networks (GANs).
• Utilized multi-step diffusion for low-resolution synthesis and a single-step generator for quality enhancement.
• Introduced normal maps to guide the synthesis process and ensure geometric consistency.

Main Results:

• NP-Hand demonstrated superior performance compared to state-of-the-art models.
• The framework successfully synthesized hand images with faithful structures and realistic details.
• Evaluations confirmed the suitability of NP-Hand for complex hand poses and textures.

Conclusions:

• NP-Hand offers an effective solution for synthesizing consistent multi-view hand images.
• The integration of diffusion models, GANs, and normal maps enhances realism and structural integrity.
• This framework advances the capabilities of AIGC for human hand representation.