11 citations · 37 across the 12 of their papers we have counts for
13 papers
Neural Particle Automata: Learning Self-Organizing Particle Dynamics
Hyunsoo Kim, Ehsan Pajouheshgar, Sabine Süsstrunk +2
We introduce Neural Particle Automata (NPA), a Lagrangian generalization of Neural Cellular Automata (NCA) from static lattices to dynamic particle systems. Unlike classical Euleri…
Exploring the Landscape of Non-Equilibrium Memories with Neural Cellular Automata
Ehsan Pajouheshgar, Aditya Bhardwaj, Nathaniel Selub +1
We investigate the landscape of many-body memories: families of local non-equilibrium dynamics that retain information about their initial conditions for thermodynamically long tim…
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…
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…
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…
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…
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,…
Mesh Neural Cellular Automata
Ehsan Pajouheshgar, Yitao Xu, Alexander Mordvintsev +3
Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-…
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata
Ehsan Pajouheshgar, Yitao Xu, Tong Zhang +1
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short…
CLIPasso: Semantically-Aware Object Sketching
Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo +5
Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scen…
Optimizing Latent Space Directions For GAN-based Local Image Editing
Ehsan Pajouheshgar, Tong Zhang, Sabine Süsstrunk
Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes. We thus present a novel objective function to evaluate the…
ChOracle: A Unified Statistical Framework for Churn Prediction
Ali Khodadadi, Seyed Abbas Hosseini, Ehsan Pajouheshgar +2
User churn is an important issue in online services that threatens the health and profitability of services. Most of the previous works on churn prediction convert the problem into…
Back to square one: probabilistic trajectory forecasting without bells and whistles
Ehsan Pajouheshgar, Christoph H. Lampert
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probabil…