Methods › Computer Vision › Image Model Blocks › AttLWB
Attentional Liquid Warping Block
AttLWB
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 Block, or AttLWB, is a module for human image synthesis GANs that propagates the source information - such as texture, style, color and face identity - in both image and feature spaces to the synthesized reference. It firstly learns similarities of the global features among all multiple sources features, and then it fuses the multiple sources features by a linear combination of the learned similarities and the multiple sources in the feature spaces. Finally, to better propagate the source identity (style, color, and texture) into the global stream, the fused source features are warped to the global stream by Spatially-Adaptive Normalization (SPADE).
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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