RevealLayer decomposes natural images into multiple RGBA layers using diffusion models with region-aware attention, occlusion-guided adaptation, and a composite loss, outperforming prior methods on a new benchmark dataset.
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AnimeAdapter is a pretrained lightweight adapter for Stable Diffusion that uses semantic-selective local attention from CLIP and pose-aware conditioning to enable zero-shot fine-grained consistent anime character generation from a single reference image.
citing papers explorer
-
RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
RevealLayer decomposes natural images into multiple RGBA layers using diffusion models with region-aware attention, occlusion-guided adaptation, and a composite loss, outperforming prior methods on a new benchmark dataset.
-
AnimeAdapter: Fine-grained and Consistent Zero-shot Anime Character Generation
AnimeAdapter is a pretrained lightweight adapter for Stable Diffusion that uses semantic-selective local attention from CLIP and pose-aware conditioning to enable zero-shot fine-grained consistent anime character generation from a single reference image.