Progressive semantic image simplification uses VLMs and a verifier to iteratively remove and inpaint scene elements while preserving photorealism, distilled into an image-to-video model for direct sequence prediction.
European Conference on Computer Vision , 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
AdaEraser introduces token-wise adaptive attention suppression in diffusion denoising to enable high-quality training-free object removal by modulating suppression according to evolving self-attention maps.
citing papers explorer
-
Progressive Photorealistic Simplification
Progressive semantic image simplification uses VLMs and a verifier to iteratively remove and inpaint scene elements while preserving photorealism, distilled into an image-to-video model for direct sequence prediction.
-
AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression
AdaEraser introduces token-wise adaptive attention suppression in diffusion denoising to enable high-quality training-free object removal by modulating suppression according to evolving self-attention maps.