no code implementations • 14 May 2024 • Weili Nie, Sifei Liu, Morteza Mardani, Chao Liu, Benjamin Eckart, Arash Vahdat
To leverage the compositionality of large language models (LLMs), we introduce a new in-context learning approach to generate blob representations from text prompts.
2 code implementations • 3 Oct 2023 • Batu Ozturkler, Chao Liu, Benjamin Eckart, Morteza Mardani, Jiaming Song, Jan Kautz
However, diffusion models require careful tuning of inference hyperparameters on a validation set and are still sensitive to distribution shifts during testing.
no code implementations • CVPR 2021 • Benjamin Eckart, Wentao Yuan, Chao Liu, Jan Kautz
In this work, we introduce a general method for 3D self-supervised representation learning that 1) remains agnostic to the underlying neural network architecture, and 2) specifically leverages the geometric nature of 3D point cloud data.
no code implementations • CVPR 2016 • Benjamin Eckart, Kihwan Kim, Alejandro Troccoli, Alonzo Kelly, Jan Kautz
In this paper we introduce a method for constructing compact generative representations of PCD at multiple levels of detail.