64 citations · 211 across the 52 of their papers we have counts for
11 papers · 1 filter
Motion Flow Matching for Human Motion Synthesis and Editing
Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma +7
Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawba…
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
Samuele Papa, Riccardo Valperga, David Knigge +4
Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, a number of works h…
Data Augmentations in Deep Weight Spaces
Aviv Shamsian, David W. Zhang, Aviv Navon +10
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various field…
Latent Field Discovery In Interacting Dynamical Systems With Neural Fields
Miltiadis Kofinas, Erik J. Bekkers, Naveen Shankar Nagaraja +1
Systems of interacting objects often evolve under the influence of field effects that govern their dynamics, yet previous works have abstracted away from such effects, and assume t…
Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
Mohammadreza Salehi, Efstratios Gavves, Cees G. M. Snoek +1
Spatially dense self-supervised learning is a rapidly growing problem domain with promising applications for unsupervised segmentation and pretraining for dense downstream tasks. D…
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Samuele Papa, David M. Knigge, Riccardo Valperga +4
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex…