ICML 2026. We first construct SA-Z, a large-scale dataset enriched with explicit occlusion ordering and pixel-level annotations. Building upon our proposed dataset, we introduce OcclusionFormer, a novel occlusion-aware Diffusion Transformer framework that explicitly models Z-order priority by decoupling instances and compositing them via volume rendering.</p>\n","updatedAt":"2026-05-21T03:50:20.915Z","author":{"_id":"67ff29ecbf6889a333c69c7a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67ff29ecbf6889a333c69c7a/zilMQrxIgUKYvHBVCHaKL.jpeg","fullname":"Henghui Ding","name":"HenghuiDing","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8134542107582092},"editors":["HenghuiDing"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/67ff29ecbf6889a333c69c7a/zilMQrxIgUKYvHBVCHaKL.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.21343","authors":[{"_id":"6a0e808b164dbbc68a26c59c","name":"Ziye Li","hidden":false},{"_id":"6a0e808b164dbbc68a26c59d","name":"Henghui Ding","hidden":false}],"publishedAt":"2026-05-20T00:00:00.000Z","submittedOnDailyAt":"2026-05-21T00:00:00.000Z","title":"OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation","submittedOnDailyBy":{"_id":"67ff29ecbf6889a333c69c7a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67ff29ecbf6889a333c69c7a/zilMQrxIgUKYvHBVCHaKL.jpeg","isPro":false,"fullname":"Henghui Ding","user":"HenghuiDing","type":"user","name":"HenghuiDing"},"summary":"Recent layout-to-image models have achieved remarkable progress in spatial controllability. 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OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation
Abstract
OcclusionFormer addresses inter-object occlusion challenges in layout-to-image generation by modeling explicit Z-order priority through diffusion transformers and volume rendering techniques.
AI-generated summary
Recent layout-to-image models have achieved remarkable progress in spatial controllability. However, they still struggle with inter-object occlusion. When bounding boxes overlap, most existing methods lack explicit occlusion information, which makes the generation in intersection regions inherently ambiguous and hinders the determination of complex occlusion relationships. As a result, they often produce entangled textures or physically inconsistent layering in the overlapped areas. To address this issue, we first construct SA-Z, a large-scale dataset enriched with explicit occlusion ordering and pixel-level annotations. Building upon our proposed dataset, we introduce OcclusionFormer, a novel occlusion-aware Diffusion Transformer framework that explicitly models Z-order priority by decoupling instances and compositing them via volume rendering. Furthermore, to ensure fine-grained spatial precision, we introduce a queried alignment loss that explicitly supervises individual instances and enhances semantic consistency. The proposed method effectively reduces ambiguity in overlapping regions, enforces correct occlusion dependencies, and preserves structural integrity, leading to substantial accuracy gains across diverse scenes.
Community
ICML 2026. We first construct SA-Z, a large-scale dataset enriched with explicit occlusion ordering and pixel-level annotations. Building upon our proposed dataset, we introduce OcclusionFormer, a novel occlusion-aware Diffusion Transformer framework that explicitly models Z-order priority by decoupling instances and compositing them via volume rendering.
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Cite arxiv.org/abs/2605.21343 in a model README.md to link it from this page.
Cite arxiv.org/abs/2605.21343 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2605.21343 in a Space README.md to link it from this page.
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