3 citations · 10 across the 6 of their papers we have counts for
6 papers
UnCommon Objects in 3D
Xingchen Liu, Piyush Tayal, Jianyuan Wang +10
We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-reso…
Meta 3D Gen
Raphael Bensadoun, Tom Monnier, Yanir Kleiman +17
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3…
Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials
Yawar Siddiqui, Tom Monnier, Filippos Kokkinos +8
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared…
Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects
Raphael Bensadoun, Yanir Kleiman, Idan Azuri +4
The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and…
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht +4
Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somew…
Mosaic-SDF for 3D Generative Models
Lior Yariv, Omri Puny, Natalia Neverova +2
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a…