RatioMorph: Controllable Diffusion Framework for Automotive Viewpoint and Proportion Manipulation in Vehicle Design

Abstract

Designing vehicle exteriors requires repeated refinement of key proportions and viewpoints, a process traditionally reliant on manual sketching, which is often time-consuming and inefficient in early concept stages. To accelerate the design process, we are exploring the potential of utilizing AI for ideation in these early stages. However, it remains a challenging task to control proportions and maintain a fixed perspective when generating images using AI. To address these limitations, we present RatioMorph, a controllable image generation system that enables manipulation of vehicle proportions and viewpoints when generating images by AI. RatioMorph comprises two core modules. Car2BoxNet is a depth estimation model that transforms real photographs into structured box-style depth maps that capture the geometric layout of the vehicle. Box2CarNet is a diffusion-based image generator fine-tuned to produce vehicle designs that adhere to the provided geometric conditions. Both Car2BoxNet and Box2CarNet are trained on a synthetic dataset curated through automated filtering based on geometric alignment and visual quality. Evaluated within a production-adjacent automotive design workflow, RatioMorph significantly reduced early-stage design iteration time and enabled exploratory workflows that were difficult with previous AI workflows. This work introduces a domain-specific, controllable diffusion-based generation system tailored for automotive design, enabling manipulation of vehicle viewpoint and proportion. It demonstrates strong potential to accelerate early-stage workflows and outlines a path toward industrial deployment, with phased integration into production environments currently underway.