Lookahead Mask-Topology Intervention Optimization (LMTIO): Predicting Downstream Inpainting Quality to Select Adaptive Mask-Topology Actions

Diffusion-based image inpainting reconstructs missing image content by progressively generating information within regions specified by a mask. The geometry and spatial arrangement of missing regions can influence the difficulty of reconstruction, particularly when a mask contains multiple disconnected components, narrow structures, complex boundaries, or regions that interact through shared objects and background textures. Existing inpainting approaches primarily focus on conditional generation, mask-aware feature processing, boundary refinement, contextual information aggregation, and iterative sampling. However, the decision concerning how the topology of a missing region should be organized during generation remains a potentially useful dimension for adaptive intervention.

Lookahead Mask-Topology Intervention Optimization (LMTIO) investigates whether predicting the downstream consequences of alternative mask-topology configurations can provide useful information for selecting inpainting actions. Instead of treating the input mask as an entirely fixed spatial structure, LMTIO represents the masked area as a collection of connected regions and associated relationships. It introduces three principal intervention categories: region splitting, region merging, and region-processing reordering. Candidate interventions are evaluated through preview inpainting computations, with particular attention to predicted background continuity, object completeness, boundary consistency, texture continuity, and computational cost.

The central mechanism is a preview-based decision process. Given an input image, an initial mask, and a current inpainting state, LMTIO generates alternative topology configurations, evaluates their prospective reconstruction trajectories, and selects an intervention according to a multi-objective decision function. The selected topology is then used to guide subsequent generation. The framework can be implemented with pretrained diffusion inpainting models without necessarily modifying their original network architectures.

This paper presents the conceptual framework, mathematical formulation, candidate intervention operators, preview evaluation mechanism, algorithmic procedure, implementation considerations, experimental hypotheses, and a controlled evaluation protocol. It distinguishes topology-action selection from conventional mask refinement, fixed mask decomposition, and sequential inpainting. The proposed experiments are designed to investigate whether adaptive topology selection provides measurable benefits over fixed-mask and non-lookahead alternatives, particularly for complex missing regions.

No experimental results are claimed in this paper. The proposed hypotheses remain to be evaluated through controlled experiments.