InsertDiffusion: Identity-Preserving Visualization of Objects through a Training-Free Diffusion Architecture

Phillip Mueller, Lars Mikelsons, Jannik Wiese, Ioan-Daniel Craciun

Abstract:

Recent advancements in image synthesis are fueled by the advent
of large-scale diffusion models. Yet, integrating realistic object visualizations
seamlessly into new or existing backgrounds without extensive
training remains a challenge. The purpose of this work is to develop a
customizable approach that simplifies object insertion while maintaining
identity and structural integrity, making high-quality visual compositions
more accessible for engineering, design, and marketing applications. We
therefore introduce InsertDiffusion, a novel training-free diffusion architecture
that efficiently embeds objects into images while preserving their
structural and identity characteristics. Our approach utilizes off-the-shelf
generative models and eliminates the need for fine-tuning, making it ideal
for rapid and adaptable visualizations in product design and marketing.
We demonstrate superior performance over existing methods in terms of
image realism and alignment with input conditions. By decomposing the
generation task into independent steps, InsertDiffusion offers a scalable
solution that extends the capabilities of diffusion models for practical
applications, achieving high-quality visualizations that maintain the authenticity
of the original objects.