Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping

TL;DR

Venus introduces a new framework and dataset for enhancing multimodal large language models with aesthetic guidance and cropping abilities, enabling more professional and interactive photo refinement.

Contribution

We present AesGuide, the first large-scale aesthetic guidance dataset, and Venus, a two-stage framework that significantly improves AG and cropping in multimodal models.

Findings

Venus achieves state-of-the-art aesthetic cropping performance.

The framework enables interpretable and interactive aesthetic refinement.

Extensive experiments validate the effectiveness of Venus.

Abstract

The widespread use of smartphones has made photography ubiquitous, yet a clear gap remains between ordinary users and professional photographers, who can identify aesthetic issues and provide actionable shooting guidance during capture. We define this capability as aesthetic guidance (AG) -- an essential but largely underexplored domain in computational aesthetics. Existing multimodal large language models (MLLMs) primarily offer overly positive feedback, failing to identify issues or provide actionable guidance. Without AG capability, they cannot effectively identify distracting regions or optimize compositional balance, thus also struggling in aesthetic cropping, which aims to refine photo composition through reframing after capture. To address this, we introduce AesGuide, the first large-scale AG dataset and benchmark with 10,748 photos annotated with aesthetic scores, analyses, and guidance. Building upon it, we propose Venus, a two-stage framework that first empowers MLLMs with AG capability through progressively complex aesthetic questions and then activates their aesthetic cropping power via CoT-based rationales. Extensive experiments show that Venus substantially improves AG capability and achieves state-of-the-art (SOTA) performance in aesthetic cropping, enabling interpretable and interactive aesthetic refinement across both stages of photo creation. Code is available at https://github.com/PKU-ICST-MIPL/Venus_CVPR2026.