ShadowEdit: Relation-Aware Modeling for Selective Shadow Removal

Existing shadow removal formulations assume that spatially localized shadows correspond to a uniquely defined restoration target, leaving intervention intent unspecified in multi-shadow scenes, yet a single shadowed observation may correspond to multiple plausible interventions, rendering conventional all-shadow restoration formulations fundamentally under-specified. We recast selective shadow removal as a relation-indexed intervention problem, where caster–shadow–receiver relations define an explicit intervention representation for target modification and effect preservation. ShadowEdit separates intervention specification from appearance reconstruction and compiles relation-level actions into constrained image updates, constraining image changes to the compiled permission support while evaluating preservation against action-specific references. Under exact Kubric counterfactuals, ShadowEdit reduces overlap error by 26.5% relative to a full-information spatial control with identical relation/action inputs and output permissions. Mechanism-aligned analyses show that action binding specifies intervention identity, protection constraints preserve unselected effects, and permission grounding restricts unintended edits. On DESOBAv2, ShadowEdit reduces overlap error by 19.4% over an equal-information spatial control under matched supervision and output permissions and supports held-out combinations of seen relation primitives, while a separately trained relation-free restoration variant of ShadowEdit achieves the highest full-image PSNR among evaluated methods on ISTD+ and SRD. Code is available in the supplementary materials.