VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generation
This addresses the problem of limited training data and diverse user expectations in content creation, offering a versatile tool for artists and designers.
The paper tackled generating realistic images from hand-drawings by proposing VisioBlend, a diffusion-based framework that allows user control over faithfulness to input sketches and strokes, achieving state-of-the-art performance in realism and flexibility.
Generating images from hand-drawings is a crucial and fundamental task in content creation. The translation is challenging due to the infinite possibilities and the diverse expectations of users. However, traditional methods are often limited by the availability of training data. Therefore, VisioBlend, a unified framework supporting three-dimensional control over image synthesis from sketches and strokes based on diffusion models, is proposed. It enables users to decide the level of faithfulness to the input strokes and sketches. VisioBlend achieves state-of-the-art performance in terms of realism and flexibility, enabling various applications in image synthesis from sketches and strokes. It solves the problem of data availability by synthesizing new data points from hand-drawn sketches and strokes, enriching the dataset and enabling more robust and diverse image synthesis. This work showcases the power of diffusion models in image creation, offering a user-friendly and versatile approach for turning artistic visions into reality.