Text to 3D
64 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Text to 3D
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Libraries
Use these libraries to find Text to 3D models and implementations
T$^3$Bench
Most implemented papers
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding and interaction within human-AI and multi-agent AI frameworks.
Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only
To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN).
Magic3D: High-Resolution Text-to-3D Content Creation
chinhsuanwu/dreamfusionacc
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CVPR 2023
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views
In this work, we study the challenging task of lifting a single image to a 3D object and, for the first time, demonstrate the ability to generate a plausible 3D object with 360{\deg} views that correspond well with the given reference image.
Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation
We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field.
SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation
yccyenchicheng/SDFusion
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CVPR 2023
To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, text, partially observed shapes and combinations of these, further allowing to adjust the strength of each input.
Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
KU-CVLAB/3DFuse
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Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting.
Vox-E: Text-guided Voxel Editing of 3D Objects
TAU-VAILab/Vox-E
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ICCV 2023
Our method takes oriented 2D images of a 3D object as input and learns a grid-based volumetric representation of it.
Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models
lukashoel/text2room
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ICCV 2023
We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input.
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.
