Aes6D: A Dataset and Benchmark for Aesthetic-Guided 6-DoF Image Editing

In real-world photography, the subject and scene content are often determined by the shooting environment and creative intent, creating a need to improve image composition and visual presentation through fine-grained viewpoint adjustment while preserving the original content. To this end, we introduce Aesthetic-Guided 6-DoF Image Editing. The task presents two key challenges: composition optimization typically requires achieving substantial improvement through small viewpoint changes within a limited camera-motion range, while real viewpoint adjustment involves both 3D translation and 3D rotation and thus requires full 6-DoF modeling. However, there is no dataset that couples fine-grained 6-DoF camera changes with substantial composition improvement. Aesthetic priors are particularly valuable for this task, as they indicate which compositional factors most affect visual quality and how they should be adjusted. Reliable supervision should therefore use aesthetic priors to guide the corresponding DoFs and motion directions, while covering the full 6-DoF space of translations, rotations, and their combinations. Based on this insight, we construct Aes6D, a dataset and benchmark for this task. Aes6D covers nine composition patterns, provides complete 6-DoF supervision with both Single and Multi samples, and uses aesthetic guidance to make small viewpoint changes consistently produce substantial and compositionally meaningful differences. Building on Aes6D, we further conduct an initial exploration of structured 6-DoF learning with DoF-GRPO, which introduces per-DoF geometric rewards, field-wise relative advantages, and token-level credit assignment to better align GRPO with the geometric structure of 6-DoF camera parameters. Experiments show that models trained on Aes6D achieve substantial composition improvement while preserving scene content, outperforming existing composition and image editing methods and validating Aes6D for this task.