PSGNet: Pure Smoke Image Generation With Gradient and Style Learning

The realistic and controllable generation of pure smoke is critical for smoke image editing, smoke visual special effects generation, and smoke data synthesizing within security scenarios. It is a relatively underexplored topic and continues to present significant challenges. Existing methods face challenges in the generation of smoke with intricate details and the regulation of various smoke styles. In this paper, a Pure Smoke image Generation Network (PSGNet) is proposed with a gradient and style learning approach to generate realistic and controllable smoke images. To achieve flexibility in control across the spatial dimension, the smoke shape mask is used to encode spatial details, such as the location and contour of the smoke, along with other related properties. To enhance the physical realism of synthesized smoke, a novel gradient-based learning framework is proposed to generate smoke gradient features, highlighting a special focus on explicitly encoding and exploiting gradient information. This framework uses a smoke gradient learning architecture that captures the subtle structures and patterns characteristic of real smoke, enabling the generation of highly realistic smoke with rich, fine-scale detail. In addition, a spatially aware style learning strategy is proposed to provide fine-grained control over smoke attributes such as density, color, and overall look. It is able to effectively model style features across both channel and spatial dimensions, thereby enabling spatially aware style manipulation. By combining the gradient module with this style learning framework, the method produces smoke that exhibits rich visual details and customizable image styles. Experiments conducted on six benchmark datasets demonstrate that the proposed PSGNet significantly outperforms the state-of-the-art approaches.