261 citations · 897 across the 19 of their papers we have counts for
28 papers
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…
UVDoc: Neural Grid-based Document Unwarping
Floor Verhoeven, Tanguy Magne, Olga Sorkine-Hornung
Restoring the original, flat appearance of a printed document from casual photographs of bent and wrinkled pages is a common everyday problem. In this paper we propose a novel meth…
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
Robin Magnet, Jing Ren, Olga Sorkine-Hornung +1
We introduce pointwise map smoothness via the Dirichlet energy into the functional map pipeline, and propose an algorithm for optimizing it efficiently, which leads to high-quality…
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…
Computational Pattern Making from 3D Garment Models
Nico Pietroni, Corentin Dumery, Raphael Guenot-Falque +3
We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization…
SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation
Amir Hertz, Or Perel, Raja Giryes +2
Neural implicit fields are quickly emerging as an attractive representation for learning based techniques. However, adopting them for 3D shape modeling and editing is challenging.…
Mesh Draping: Parametrization-Free Neural Mesh Transfer
Amir Hertz, Or Perel, Raja Giryes +2
Despite recent advances in geometric modeling, 3D mesh modeling still involves a considerable amount of manual labor by experts. In this paper, we introduce Mesh Draping: a neural…
Sparsity-Specific Code Optimization using Expression Trees
Philipp Herholz, Xuan Tang, Teseo Schneider +3
We introduce a code generator that converts unoptimized C++ code operating on sparse data into vectorized and parallel CPU or GPU kernels. Our approach unrolls the computation into…
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
Wang Yifan, Lukas Rahmann, Olga Sorkine-Hornung
We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method repr…
SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Amir Hertz, Or Perel, Raja Giryes +2
Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands. We present a spatially adaptive p…
Learning Skeletal Articulations with Neural Blend Shapes
Peizhuo Li, Kfir Aberman, Rana Hanocka +3
Animating a newly designed character using motion capture (mocap) data is a long standing problem in computer animation. A key consideration is the skeletal structure that should c…
Dev2PQ: Planar Quadrilateral Strip Remeshing of Developable Surfaces
Floor Verhoeven, Amir Vaxman, Tim Hoffmann +1
We introduce an algorithm to remesh triangle meshes representing developable surfaces to planar quad dominant meshes. The output of our algorithm consists of planar quadrilateral (…
Designing Personalized Garments with Body Movement
Katja Wolff, Philipp Herholz, Verena Ziegler +3
The standardized sizes used in the garment industry do not cover the range of individual differences in body shape for most people, leading to ill-fitting clothes, high return rate…
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Wang Yifan, Shihao Wu, Cengiz Oztireli +1
Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can encode a high quality surface with intricate details into the parameters…
Skeleton-Aware Networks for Deep Motion Retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski +3
We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Imp…
Neural Cages for Detail-Preserving 3D Deformations
Wang Yifan, Noam Aigerman, Vladimir G. Kim +2
We propose a novel learnable representation for detail-preserving shape deformation. The goal of our method is to warp a source shape to match the general structure of a target sha…
Differentiable Surface Splatting for Point-based Geometry Processing
Wang Yifan, Felice Serena, Shihao Wu +2
We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. Gradients for point locations and normals are carefully designed to han…
Patch-based Progressive 3D Point Set Upsampling
Wang Yifan, Shihao Wu, Hui Huang +2
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by…
A Fully Progressive Approach to Single-Image Super-Resolution
Yifan Wang, Federico Perazzi, Brian McWilliams +3
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each ca…
Deformation Capture via Soft and Stretchable Sensor Arrays
Oliver Glauser, Daniele Panozzo, Otmar Hilliges +1
We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line of sight. Our first contribution is a low-cost…