no code implementations • 26 Mar 2024 • Sherwin Bahmani, Xian Liu, Yifan Wang, Ivan Skorokhodov, Victor Rong, Ziwei Liu, Xihui Liu, Jeong Joon Park, Sergey Tulyakov, Gordon Wetzstein, Andrea Tagliasacchi, David B. Lindell
We learn local deformations that conform to the global trajectory using supervision from a text-to-video model.
1 code implementation • 29 Nov 2023 • Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, David B. Lindell
Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes.
no code implementations • ICCV 2023 • Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Xingguang Yan, Gordon Wetzstein, Leonidas Guibas, Andrea Tagliasacchi
In this work, we introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained using single-view images.
1 code implementation • 10 Aug 2022 • Sherwin Bahmani, Oliver Hahn, Eduard Zamfir, Nikita Araslanov, Daniel Cremers, Stefan Roth
The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation.
1 code implementation • 29 Jun 2022 • Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas Guibas, Luc van Gool, Radu Timofte
Generative models have emerged as an essential building block for many image synthesis and editing tasks.
2 code implementations • CVPR 2022 • Yuejiang Liu, Riccardo Cadei, Jonas Schweizer, Sherwin Bahmani, Alexandre Alahi
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting.
no code implementations • 29 Sep 2021 • Sherwin Bahmani, Oliver Hahn, Eduard Sebastian Zamfir, Nikita Araslanov, Stefan Roth
In this work, we empirically study an adaptive inference strategy for semantic segmentation that adjusts the model to the test sample before producing the final prediction.