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pith:L64MDESS

pith:2026:L64MDESSGDNLRA5WXPRL6FZI42

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Texture Regenerating and Grafting Using Genome-Driven Neural Cellular Automata

Alexandra B\u{a}icoianu, Ioana Cristina Plajer, Mirela-Magdalena Catrina

Neural cellular automata can self-regenerate damaged textures and graft distinct ones together by initializing genome channels at inference time.

arxiv:2605.13630 v1 · 2026-05-13 · cs.NE

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Claims

C1strongest claim

A novel training methodology enables robust self-regeneration of textures in damaged regions, and a grafting technique achieves seamless combination of distinct textures efficiently during the inference phase without requiring specialized retraining, through precise initialization of the NCA's genome channels.

C2weakest assumption

That precise initialization of genome channels in the NCA allows seamless, high-quality grafting of arbitrary distinct textures at inference time without retraining or visible artifacts, and that the self-regeneration mechanism remains robust across varied damage patterns and texture complexities.

C3one line summary

Neural cellular automata can be trained for robust self-regeneration of damaged textures and for efficient grafting of multiple textures at inference time via genome channel initialization.

References

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[1] Texture Regenerating and Grafting Using Genome-Driven Neural Cellular Automata 2026 · arXiv:2605.13630

[2] MA TERIALS AND METHODS 2.1. Previous work The fundamental architecture of the NCA employed for tex- ture synthesis is extensively detailed in [13] and will thus be concisely reviewed herein for contex

[3] Here, damaging refers to randomizing the state vectors of cells con- tained within a circular region of radius 15 to 25 pixels

[4] The examples utilize an NCA trained on sets of 4 or 8 textures, as those illustrated in Fig

[5] This approach involves initializing a single NCA at timestampt= 0

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Texture Regenerating and Grafting Using Genome-Driven Neural Cellular Automata

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First computed	2026-05-18T02:44:17.761789Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

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5fb8c1925230dab883b6bbe2bf1728e6ad52366c79e4f3beb43a3067d16d4965

Aliases

arxiv: 2605.13630 · arxiv_version: 2605.13630v1 · doi: 10.48550/arxiv.2605.13630 · pith_short_12: L64MDESSGDNL · pith_short_16: L64MDESSGDNLRA5W · pith_short_8: L64MDESS

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/L64MDESSGDNLRA5WXPRL6FZI42 \
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Canonical record JSON

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