Reference Object Guided AI Image Generation: Advances, Methods, and Home Furnishing Applications

This article reviews the latest progress of reference‑object‑based AI image generation (AIGC). It first outlines the rapid development of text‑to‑image diffusion models such as Stable Diffusion and the need for additional control mechanisms to better align generated images with user intent.

Reference objects constitute a powerful control dimension: by providing one or more exemplar images, the model can preserve the identity and visual characteristics of the target object while synthesising new scenes. The approach enables use cases like virtual try‑on and, more relevantly, home‑furnishing recommendation, where users can preview furniture placed in their own rooms.

The paper examines two representative studies. The Dreambooth method expands the model’s vocabulary with a unique identifier linked to a small set of reference images, allowing subject‑driven generation across varied poses, lighting, and backgrounds. The Blip‑diffusion method trains a visual‑text encoder to produce object embeddings that can be injected into the diffusion process, achieving zero‑shot synthesis and fast inference comparable to standard diffusion sampling.

Both methods have trade‑offs: Dreambooth requires time‑consuming fine‑tuning and may overfit with few images, while Blip‑diffusion relies on a pre‑trained encoder and still faces challenges in complex scenes.

In the home‑furnishing scenario, practical challenges include viewpoint mismatch between product images and user room photos, lack of depth/size information, and variable image quality of catalog photos. By combining 3D reconstruction with AIGC techniques, the authors demonstrate prototype results that blend product images into user environments.

The article concludes that reference‑object‑driven AIGC holds significant commercial potential for e‑commerce, improving user experience and product presentation, and warrants further research.