OcclusionFormer adds explicit Z-order modeling via a new SA-Z dataset and volume-rendering compositing in a diffusion transformer to resolve occlusion ambiguities in layout-grounded image synthesis.
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2026 2verdicts
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Energy-based model with covariance regularization computes normalized posteriors for linear inverse problems without retraining, enabling adaptive sampling and blind estimation on image datasets.
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OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation
OcclusionFormer adds explicit Z-order modeling via a new SA-Z dataset and volume-rendering compositing in a diffusion transformer to resolve occlusion ambiguities in layout-grounded image synthesis.
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Learning Normalized Energy Models for Linear Inverse Problems
Energy-based model with covariance regularization computes normalized posteriors for linear inverse problems without retraining, enabling adaptive sampling and blind estimation on image datasets.