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Found 25 papers, 18 papers with code
iSeg: Interactive 3D Segmentation via Interactive Attention
We present iSeg, a new interactive technique for segmenting 3D shapes.
AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model
Ambigrams are calligraphic designs that have different meanings depending on the viewing orientation.
3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation
In this work we develop 3D Paintbrush, a technique for automatically texturing local semantic regions on meshes via text descriptions.
HyperFields: Towards Zero-Shot Generation of NeRFs from Text
We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning.
TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis
The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently explored in the motion domain.
TextDeformer: Geometry Manipulation using Text Guidance
Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients.
3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions
A key feature of our system is the ability to interpret "out-of-domain" localizations.
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization
We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization.
LoopDraw: a Loop-Based Autoregressive Model for Shape Synthesis and Editing
There is no settled universal 3D representation for geometry with many alternatives such as point clouds, meshes, implicit functions, and voxels to name a few.
TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes.
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.
NeuralMLS: Geometry-Aware Control Point Deformation
In this work, we opt to learn the weighting function, by training a neural network on the control points from a single input shape, and exploit the innate smoothness of neural networks.
Text2Mesh: Text-Driven Neural Stylization for Meshes
In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP.
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The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference
We propose the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign fine-grained semantic labels to regions of a 3D shape.
Z2P: Instant Visualization of Point Clouds
We present a technique for visualizing point clouds using a neural network.
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.
Orienting Point Clouds with Dipole Propagation
In the global phase, we propagate the orientation across all coherent patches using a dipole propagation.
Self-Sampling for Neural Point Cloud Consolidation
We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud.
Deep Geometric Texture Synthesis
Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus.
Point2Mesh: A Self-Prior for Deformable Meshes
We optimize the network weights to deform an initial mesh to shrink-wrap a single input point cloud.
PointGMM: a Neural GMM Network for Point Clouds
We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud.
Blind Visual Motif Removal from a Single Image
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image.
MeshCNN: A Network with an Edge
In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes.
ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning
The process of aligning a pair of shapes is a fundamental operation in computer graphics.
Fast and easy blind deblurring using an inverse filter and PROBE
PROBE (Progressive Removal of Blur Residual) is a recursive framework for blind deblurring.
