Methods › Computer Vision › Generative Adversarial Networks › Attentional Liquid Warping GAN
Attentional Liquid Warping GAN
Introduced by Wen Liu et al. in Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Attentional Liquid Warping GAN is a type of generative adversarial network for human image synthesis that utilizes a AttLWB block, which is a 3D body mesh recovery module that disentangles pose and shape. To preserve the source information, such as texture, style, color, and face identity, the Attentional Liquid Warping GAN with AttLWB propagates the source information in both image and feature spaces to the synthesized reference.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis 18 Nov 2020 · 2 repositories · arXiv:2011.09055
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Appearance Transfer | 1 |
| Denoising | 1 |
| Image Generation | 1 |
| Novel View Synthesis | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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