Image Morphing

18 papers with code • 0 benchmarks • 0 datasets

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Latest papers with no code

DiffMorph: Text-less Image Morphing with Diffusion Models

no code yet • 1 Jan 2024

The image generation capability of our work is demonstrated through our results and a comparison of these with prompt-based image generation.

DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing

no code yet • 12 Dec 2023

Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation.

IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models

no code yet • 12 Nov 2023

We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.

Three-dimensional Bone Image Synthesis with Generative Adversarial Networks

no code yet • 26 Oct 2023

Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential.

Vulnerability of 3D Face Recognition Systems to Morphing Attacks

no code yet • 21 Sep 2023

A substantial amount of research is being done for generation of high quality face morphs along with detection of attacks from these morphs.

Measure transfer via stochastic slicing and matching

no code yet • 11 Jul 2023

The proof builds on an interpretation as a stochastic gradient descent scheme on the Wasserstein space.

Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset

no code yet • IJCRT 2022

This paper highlights the addition of a sequential layer to the traditional RESNET 18 model for computing the accuracy of an Image classification dataset.

Decision boundaries and convex hulls in the feature space that deep learning functions learn from images

no code yet • 5 Feb 2022

We study the partitioning of the domain in feature space, identify regions guaranteed to have certain classifications, and investigate its implications for the pixel space.