collaborators

6 papers

cs.CV20241 cited

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…

cs.CV20243 cited

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…

cs.CV2023

HoloFusion: Towards Photo-realistic 3D Generative Modeling

Animesh Karnewar, Niloy J. Mitra, Andrea Vedaldi +1

Diffusion-based image generators can now produce high-quality and diverse samples, but their success has yet to fully translate to 3D generation: existing diffusion methods can eit…

cs.CV2023

Replay: Multi-modal Multi-view Acted Videos for Casual Holography

Roman Shapovalov, Yanir Kleiman, Ignacio Rocco +6

We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially. Each scene is filmed in high production quality, from different viewpoints with…

cs.CV20231 cited

Real-time volumetric rendering of dynamic humans

Ignacio Rocco, Iurii Makarov, Filippos Kokkinos +4

We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos with accompanying parametric body fits. Our method can reconstruct a…

cs.CV20234 cited

HoloDiffusion: Training a 3D Diffusion Model using 2D Images

Animesh Karnewar, Andrea Vedaldi, David Novotny +1

Diffusion models have emerged as the best approach for generative modeling of 2D images. Part of their success is due to the possibility of training them on millions if not billion…