3 citations · 5 across the 11 of their papers we have counts for
12 papers
MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer
Weiyu Li, Antoine Toisoul, Tom Monnier +4
We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction, a natural choice given the d…
AssetGen: Deployable 3D Asset Generation at Interactive Speed
Dilin Wang, Xiaoyu Xiang, Kihyuk Sohn +14
While 3D generation is progressing rapidly, recent work has often focused on obtaining high-resolution assets, leaving user experience and deployability as afterthoughts. We presen…
ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion
Remy Sabathier, David Novotny, Niloy J. Mitra +1
Generating animated 3D objects is at the heart of many applications, yet most advanced works are typically difficult to apply in practice because of their limited setup, their long…
WorldGen: From Text to Traversable and Interactive 3D Worlds
Dilin Wang, Hyunyoung Jung, Tom Monnier +22
We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts. Our approach transforms natural language descr…
AutoPartGen: Autogressive 3D Part Generation and Discovery
Minghao Chen, Jianyuan Wang, Roman Shapovalov +6
We introduce AutoPartGen, a model that generates objects composed of 3D parts in an autoregressive manner. This model can take as input an image of an object, 2D masks of the objec…
WildCAT3D: Appearance-Aware Multi-View Diffusion in the Wild
Morris Alper, David Novotny, Filippos Kokkinos +2
Despite recent advances in sparse novel view synthesis (NVS) applied to object-centric scenes, scene-level NVS remains a challenge. A central issue is the lack of available clean m…
Twinner: Shining Light on Digital Twins in a Few Snaps
Jesus Zarzar, Tom Monnier, Roman Shapovalov +2
We present the first large reconstruction model, Twinner, capable of recovering a scene's illumination as well as an object's geometry and material properties from only a few posed…
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…
PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models
Minghao Chen, Roman Shapovalov, Iro Laina +4
Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, l…
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…
GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning
Animesh Karnewar, Roman Shapovalov, Tom Monnier +3
Encoding information from 2D views of an object into a 3D representation is crucial for generalized 3D feature extraction. Such features can then enable 3D reconstruction, 3D gener…