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20242026
most citedNeural Cellular Automata: From Cells to Pixels

1 citations · 1 across the 1 of their papers we have counts for

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7 papers

cs.CV20261 cited

Neural Cellular Automata: From Cells to Pixels

Ehsan Pajouheshgar, Yitao Xu, Ali Abbasi +3

Neural Cellular Automata (NCAs) are bio-inspired dynamical systems in which identical cells iteratively apply a learned local update rule to self-organize into complex patterns, ex…

cs.LG2025

Canonical Latent Representations in Conditional Diffusion Models

Yitao Xu, Tong Zhang, Ehsan Pajouheshgar +1

Conditional diffusion models (CDMs) have shown impressive performance across a range of generative tasks. Their ability to model the full data distribution has opened new avenues f…

eess.IV2025

Volumetric Temporal Texture Synthesis for Smoke Stylization using Neural Cellular Automata

Dongqing Wang, Ehsan Pajouheshgar, Yitao Xu +2

Artistic stylization of 3D volumetric smoke data is still a challenge in computer graphics due to the difficulty of ensuring spatiotemporal consistency given a reference style imag…

cs.CV2024

AdaNCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformer

Yitao Xu, Tong Zhang, Sabine Süsstrunk

Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information v…

cs.CV2024

Emergent Dynamics in Neural Cellular Automata

Yitao Xu, Ehsan Pajouheshgar, Sabine Süsstrunk

Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA). Emergent motion in the patterns created by NCA has been successfully applied t…

cs.CV2024

NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata

Ehsan Pajouheshgar, Yitao Xu, Sabine Süsstrunk

Neural Cellular Automata (NCA) is a class of Cellular Automata where the update rule is parameterized by a neural network that can be trained using gradient descent. In this paper,…