PASEdit: Portrait Aesthetic Structural Editing

Generative image editing models enable high-level structural editing of portraits for aesthetic enhancement, such as adjusting pose, viewpoint, and framing. However, existing editors still face two key challenges: deciding how to improve a structurally suboptimal portrait from a coarse request, and making effective structural changes without causing identity drift or unintended changes. We propose PASEdit, a planner-executor framework for controllable single-factor portrait structural editing across pose, viewpoint, and framing. For planning, we introduce Aesthetic Planning Post-Training (AesPT), which trains factor-specific experts with task-specific visual feedback and consolidates their capabilities into a unified planner through multi-teacher on-policy distillation. For execution, the executor first learns structural transformations from paired supervision and is then optimized for identity and non-target content preservation while maintaining instruction adherence. To provide controlled supervision for structural learning, we construct 12,422 portrait editing pairs derived from real photographic degradation patterns. Extensive experiments show that PASEdit achieves more effective structural improvements while maintaining strong identity and non-target content preservation.