203 citations · 433 across the 11 of their papers we have counts for
16 papers · 1 filter
Neural Garment Dynamics via Manifold-Aware Transformers
Peizhuo Li, Tuanfeng Y. Wang, Timur Levent Kesdogan +2
Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often…
SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling
Alexandre Binninger, Amir Hertz, Olga Sorkine-Hornung +2
We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature. Our method allows users to quickly and easily ske…
GarmentCode: Programming Parametric Sewing Patterns
Maria Korosteleva, Olga Sorkine-Hornung
Garment modeling is an essential task of the global apparel industry and a core part of digital human modeling. Realistic representation of garments with valid sewing patterns is k…
Example-based Motion Synthesis via Generative Motion Matching
Weiyu Li, Xuelin Chen, Peizhuo Li +2
We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, whi…
MoDi: Unconditional Motion Synthesis from Diverse Data
Sigal Raab, Inbal Leibovitch, Peizhuo Li +3
The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains challenging…
GANimator: Neural Motion Synthesis from a Single Sequence
Peizhuo Li, Kfir Aberman, Zihan Zhang +2
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements o…