GeoComposer improves photo framing using 3D geometry and text guidance
GeoComposer: Geometry-Grounded Photographic Composition Instruction
Computer Vision and Pattern Recognition
Summary
Taking good photos often means arranging the scene well and choosing the right viewpoint. Most earlier tools just crop photos to improve this, which limits how much the photo can change. The authors created GeoComposer, a tool that understands the 3D shape of the scene in a photo and suggests ways to improve its composition with text instructions and example images. It uses a special method that keeps the photo’s geometric details accurate while making it look better and follows the instructions well. Tests show GeoComposer’s improvements are better and more consistent than previous methods.
What this means in practice
- •For photo editing software developers: Add features that generate text and visual tips for improving photo composition while preserving 3D scene consistency.$Commercial implications: Enables photo editing tools to offer guided composition improvements with geometry-aware visuals, improving user experience and photo quality.
- •For augmented reality app developers: Create apps that guide users to frame scenes based on 3D geometry and composition rules, improving photo capture in real time.
Authors
Shuangzhi Li, Qiaoqiao Jia, Xingxin Chen, Guile Wu, Dongfeng Bai
Abstract
Photographic composition aims to provide visual guidance for improving the framing, viewpoint, and spatial arrangement of an image. Early methods primarily rely on image cropping to enhance composition, which is restricted to the viewpoint and spatial arrangement of the input image. Recent methods have explored image understanding and editing to improve composition, but they mainly focus on instruction following and aesthetic quality, overlooking the importance of 3D scene geometry consistency for photographic composition. In this work, we propose GeoComposer, a novel geometry-grounded photographic composition framework that analyzes the composition of a given image to generate textual guidance and synthesizes a visual exemplar that enhances the composition of the given image. To promote geometry-grounded composition, we propose a geometry-aware representation learning mechanism that leverages geometric priors from a visual geometry foundation model to shape the intermediate representations of the composition editing model. This mechanism preserves both global structural relationships and local fine-grained correspondences for geometry-grounded composition. Furthermore, we propose a reinforcement learning strategy guided by a hybrid reward that jointly optimizes instruction following, aesthetic quality, and geometric consistency. This enables the model to generate visual exemplars that faithfully follow the composition instructions while remaining visually appealing and geometrically consistent. Extensive experiments show the superiority of our approach over state-of-the-art methods, highlighting its effectiveness in generating visually appealing and geometrically consistent composition.