TSLP: Text driven and style transferable indoor furniture layout generation pipeline

Abstract

With the surging demand in interior design, high-quality furniture layout as-sets have become increasingly indispensable. However, creating such assets requires professional expertise in both aesthetic design and 3D modeling, leading to significant manual overhead. Consequently, there is a compelling need for generative frameworks capable of autonomous, high-quality asset generation. Existing methods focus on holistic image-to-3D synthesis or as-set retrieval, yet they yield monolithic representations that lack instance-level editability and stylistic steerability. To this end, we present TSLP, a text-driven and style-transferable pipeline for indoor furniture layout generation. Our framework first synthesizes an interior image from textual prompts via a diffusion-based model, followed by a style-transfer module for aesthetic customization. We then reconstruct decoupled 3D assets with precise pose estimation, finally employing a texture synthesis model to bake high-fidelity textures onto the generated objects. Our pipeline enables seamless 3D furniture layout synthesis from text, granting users granular control over object structure, category, and spatial positioning. By integrating a style-transfer module, our framework facilitates effortless aesthetic customization via a single style reference image, significantly enhancing both usability and adaptability.