SIFT-VTON adds explicit geometric supervision from SIFT keypoints to diffusion-based virtual try-on to improve spatial alignment and detail preservation.
In: NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications (2021)
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Map2World produces scale-consistent 3D worlds from text and arbitrary segment maps via a detail enhancer that incorporates global structure information.
PECKER uses a saliency mask to prioritize parameter updates in distillation-based unlearning, achieving shorter training times for class and concept forgetting on CIFAR-10 and STL-10 while matching prior methods' efficacy.
citing papers explorer
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SIFT-VTON: Geometric Correspondence Supervision on Cross-Attention for Virtual Try-On
SIFT-VTON adds explicit geometric supervision from SIFT keypoints to diffusion-based virtual try-on to improve spatial alignment and detail preservation.
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Map2World: Segment Map Conditioned Text to 3D World Generation
Map2World produces scale-consistent 3D worlds from text and arbitrary segment maps via a detail enhancer that incorporates global structure information.
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PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models
PECKER uses a saliency mask to prioritize parameter updates in distillation-based unlearning, achieving shorter training times for class and concept forgetting on CIFAR-10 and STL-10 while matching prior methods' efficacy.