Harmonia: Optimizing Aesthetic Gains within the Semantic Elasticity Boundary for Aesthetic Photo Reconstruction

Aesthetic Photo Reconstruction (APR) improves photo aesthetics through structural edits while preserving the original subjects and scene. However, APR inherently faces an aesthetic–semantic tension: larger structural changes may yield greater aesthetic gains but also increase the risk of altering subject identity and scene semantics. We therefore formulate APR as maximizing aesthetic gains within a photo-dependent semantic elasticity boundary. Existing methods optimize reconstruction plans only in text space, without feedback from their realized visual outcomes, making it difficult to assess semantic feasibility and optimize aesthetics within this boundary. They also lack support for intent-conditioned personalized APR. To address these limitations, we propose Harmonia, a two-stage APR framework: (1) Structured Photographic Planning Learning, which decomposes reconstruction plans into atomic photographic operations and jointly learns reference-free, reference-guided, and intent-conditioned planning; and (2) Feasible-Space Visual Optimization, which introduces Visual Aesthetic GRPO (VA-GRPO) to execute candidate plans and optimize the planner from their realized visual outcomes within the semantic elasticity boundary. We further construct Harmonia-Bench for evaluating both default and personalized APR. Experiments show that Harmonia outperforms AesFormer using only 30% of its training data even without fine-tuning the image editor, while additionally supporting personalized APR for the first time.