no code implementations • 26 Oct 2023 • Sudarshan Babu, Richard Liu, Avery Zhou, Michael Maire, Greg Shakhnarovich, Rana Hanocka
We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning.
no code implementations • 27 Jul 2023 • Zihan Zhang, Richard Liu, Kfir Aberman, Rana Hanocka
The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently explored in the motion domain.
1 code implementation • CVPR 2023 • Richard Liu, Noam Aigerman, Vladimir G. Kim, Rana Hanocka
We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization.
no code implementations • 28 Jan 2022 • Addison Wood, Jory Schossau, Nick Sabaj, Richard Liu, Mark Reimers
A profound challenge for A-Life is to construct agents whose behavior is 'life-like' in a deep way.
1 code implementation • CVPR 2022 • Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, Rana Hanocka
In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP.
Ranked #1 on Neural Stylization on Meshes
1 code implementation • 3 Jun 2020 • Johnu George, Ce Gao, Richard Liu, Hou Gang Liu, Yuan Tang, Ramdoot Pydipaty, Amit Kumar Saha
In this paper, we introduce Katib: a scalable, cloud-native, and production-ready hyperparameter tuning system that is agnostic of the underlying machine learning framework.