Semantic Probability Distribution Modeling for Diverse Semantic Image Synthesis.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Semantic image synthesis translates semantic layouts into realistic images, a challenging one-to-many mapping problem.
• Existing methods struggle with efficient, diverse semantic and instance-level multimodal synthesis.

Purpose of the Study:

• To propose a novel framework for diverse semantic image synthesis supporting both semantic and instance-level variations.
• To enhance control over image generation through instance-adaptive sampling and exemplar-based style transfer.
• To improve user interaction and generation quality for complex scenes.

Main Methods:

• Modeling class-level conditional modulation parameters as continuous probability distributions.
• Employing instance-adaptive stochastic sampling for per-instance modulation.
• Introducing prior noise remapping for supervised training and style control.
• Integrating sketch-based input and specialized generator modules (Progressive Growing, Multi-Scale Refinement).

Main Results:

• The proposed method achieves superior diversity in semantic image synthesis.
• It demonstrates comparable image quality to state-of-the-art approaches.
• Experiments confirm the framework's effectiveness across multiple datasets.

Conclusions:

• The novel framework successfully addresses the challenge of diverse semantic image synthesis.
• It offers enhanced control and flexibility for generating multimodal results.
• The approach shows significant potential for advancing realistic image generation from semantic data.