LayerBench: Benchmarking Layer Decomposition Beyond Reconstruction

Layer decomposition is fundamental to editable image manipulation, yet its evaluation remains misaligned with its purpose: a flat copy can achieve near-perfect reconstruction while being useless for editing. Existing protocols prioritize pixel fidelity, are tied to narrow distributions, and rarely validate model-based scores against human preferences. We introduce LayerBench, a comprehensive benchmark for layer decomposition that explicitly aligns evaluation with human preferences. LayerBench comprises 1.9K curated images spanning diverse domains, layer cardinalities, occlusion patterns, transparency effects, and semantic structures, together with a preference corpus of 13,979 expert pairwise comparisons used to train LayerReward. Its evaluation suite combines rule-based metrics for reconstruction, alignment, text preservation, and operational editability; model-based metrics from pretrained large vision-language models; and LayerReward, a five-dimensional evaluator that exceeds 80% overall pairwise accuracy and generalizes to out-of-distribution shadow separation. Beyond evaluation, LayerReward also serves as a reward signal for LayerCompass, a reward-guided post-training method included as part of LayerBench. Experiments show that reconstruction fidelity is necessary but insufficient for editability and human preference.