Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing
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Abstract (arXiv)
Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties are (i) to be smooth; (ii) to apply the minimal required change in the image; and (iii) to seem "real", avoiding unnatural artifacts in each image in the transition. To obtain a smooth and straightforward transition, one may adopt the well-known Wasserstein Barycenter Problem (WBP). While this approach guarantees minimal changes under the Wasserstein metric, the resulting images might seem unnatural. In this work, we propose a novel approach for image morphing that possesses all three desired properties. To this end, we define a constrained variant of the WBP that enforces the intermediate images to satisfy an image prior. We describe an algorithm that solves this problem and demonstrate it using the sparse prior and generative adversarial networks.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Dec 2019.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 1; 2025: 2.
11 papers.
- 2022Wasserstein Iterative Networks for Barycenter Estimationcited by 23
- 2023IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Modelscited by 20
- 2023Energy-Guided Continuous Entropic Barycenter Estimation for General Costscited by 12
- 2021Fixed Support Tree-Sliced Wasserstein Barycentercited by 10
- 2025Probability Density Geodesics in Image Diffusion Latent Spacecited by 9
- 2022Multiview Regenerative Morphing with Dual Flowscited by 5
- 2021Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescencecited by 2
- 2022Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernelscited by 2
- 2022BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioningcited by 2
- 2025Joint Manifold Learning and Optimal Transport for Dynamic Imagingcited by 1
- 2024On Barycenter Computation: Semi-Unbalanced Optimal Transport-based Method on Gaussianscited by 0
- 2025Joint Manifold Learning and Optimal Transport for Dynamic Imaging17 May 2025
- 2025Probability Density Geodesics in Image Diffusion Latent Space9 Apr 2025
- 2024On Barycenter Computation: Semi-Unbalanced Optimal Transport-based Method on Gaussians10 Oct 2024
- 2023IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models12 Nov 2023
- 2023Energy-Guided Continuous Entropic Barycenter Estimation for General Costs2 Oct 2023
- 2022BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning3 Dec 2022
- 2022Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels2 Dec 2022
- 2022Multiview Regenerative Morphing with Dual Flows2 Aug 2022
- 2022Wasserstein Iterative Networks for Barycenter Estimation28 Jan 2022
- 2021Fixed Support Tree-Sliced Wasserstein Barycenter8 Sep 2021
- 2021Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence22 Jan 2021