RefAdGen: High-Fidelity Advertising Image Generation

The rapid advancement of AIGC techniques has unlocked opportunities in generating diverse and compelling advertisement images based on referenced product images and textual scene descriptions. This capability substantially reduces human labor and production costs in traditional marketing workflows. However, existing AIGC techniques either demand extensive fine-tuning for each referenced image to achieve high fidelity, or they struggle to maintain fidelity across diverse products, making them impractical for e-commerce and marketing industries. To tackle this limitation, we first construct AdProd-100K, a large-scale advertising image generation dataset. A key innovation in its construction is our dual data augmentation strategy, which fosters robust, 3D-aware representations crucial for realistic and high-fidelity image synthesis. Leveraging this dataset, we propose RefAdGen, which ensures high fidelity by integrating product features into the text-described scene using the Mask-Guided Attention Fusion (MGAF) mechanism. Extensive experiments conducted on AdProd-100K demonstrate that RefAdGen achieves state-of-the-art performance in both product fidelity and overall generation quality, offering a scalable and cost-effective alternative to traditional workflows while achieving remarkable visual results.