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Found 18 papers, 14 papers with code
Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images.
SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling
We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature.
Example-based Motion Synthesis via Generative Motion Matching
At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches.
UVDoc: Neural Grid-based Document Unwarping
In this paper we propose a novel method for grid-based single-image document unwarping.
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
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 results in challenging settings.
MoDi: Unconditional Motion Synthesis from Diverse Data
In this work, we present MoDi -- a generative model trained in an unsupervised setting from an extremely diverse, unstructured and unlabeled dataset.
GANimator: Neural Motion Synthesis from a Single Sequence
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence.
SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation
Neural implicit fields are quickly emerging as an attractive representation for learning based techniques.
Mesh Draping: Parametrization-Free Neural Mesh Transfer
The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh.
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
We present implicit displacement fields, a novel representation for detailed 3D geometry.
Learning Skeletal Articulations with Neural Blend Shapes
Furthermore, we propose neural blend shapes--a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning.
SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands.
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Neural implicit functions have emerged as a powerful representation for surfaces in 3D.
Skeleton-Aware Networks for Deep Motion Retargeting
In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.
Neural Cages for Detail-Preserving 3D Deformations
The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source.
Differentiable Surface Splatting for Point-based Geometry Processing
We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds.
Patch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling.
A Fully Progressive Approach to Single-Image Super-Resolution
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.
Ranked #14 on
Image Super-Resolution
on BSD100 - 4x upscaling
