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Found 10 papers, 9 papers with code
A Neural Space-Time Representation for Text-to-Image Personalization
We observe that one can significantly improve the convergence and visual fidelity of the concept by introducing a textual bypass, where our neural mapper additionally outputs a residual that is added to the output of the text encoder.
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
We show that using proxies allows a wide variety of editing options, such as adjusting the placement of each independent object, removing objects from a scene, or refining an object.
TEXTure: Text-Guided Texturing of 3D Shapes
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes.
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
This unique combination of text and shape guidance allows for increased control over the generation process.
Ranked #3 on
Text to 3D
on T$^3$Bench
TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes.
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.
Z2P: Instant Visualization of Point Clouds
We present a technique for visualizing point clouds using a neural network.
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.
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.
