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Aligning Latent and Image Spaces to Connect the Unconnectable

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arxiv 2104.06954 v1 pith:ZTDJNPL7 submitted 2021-04-14 cs.CV cs.AI

classification cs.CVcs.AI
keywords imageslatentinfinitecodescomplexdiversegeneratorhigh-resolution
verification ladder T0 review T1 audit T2 compute T3 formal
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In this work, we develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant generator with synchronous interpolations in the image and latent spaces. Latent codes, when sampled, are positioned on the coordinate grid, and each pixel is computed from an interpolation of the nearby style codes. We modify the AdaIN mechanism to work in such a setup and train the generator in an adversarial setting to produce images positioned between any two latent vectors. At test time, this allows for generating complex and diverse infinite images and connecting any two unrelated scenes into a single arbitrarily large panorama. Apart from that, we introduce LHQ: a new dataset of \lhqsize high-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times in terms of quality and diversity of the produced infinite images. The project page is located at https://universome.github.io/alis.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LeDiFlow: Learned Distribution-guided Flow Matching to Accelerate Image Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A learned distribution prior, predicted by a VAE-style decoder, lets flow-matching image models generate in fewer ODE steps than with a Gaussian prior.

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