no code implementations • 20 Jul 2024 • Sanjeev Muralikrishnan, Niladri Shekhar Dutt, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy J. Mitra
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer.
no code implementations • 3 Apr 2024 • Duygu Ceylan, Valentin Deschaintre, Thibault Groueix, Rosalie Martin, Chun-Hao Huang, Romain Rouffet, Vladimir Kim, Gaëtan Lassagne
We present MatAtlas, a method for consistent text-guided 3D model texturing.
no code implementations • 1 Dec 2021 • Kai Wang, Paul Guerrero, Vladimir Kim, Siddhartha Chaudhuri, Minhyuk Sung, Daniel Ritchie
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning.
1 code implementation • CVPR 2022 • Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy Mitra
We investigate the problem of training generative models on a very sparse collection of 3D models.
1 code implementation • CVPR 2021 • Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra
Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry.
1 code implementation • ECCV 2020 • Hung-Yu Tseng, Matthew Fisher, Jingwan Lu, Yijun Li, Vladimir Kim, Ming-Hsuan Yang
People often create art by following an artistic workflow involving multiple stages that inform the overall design.
no code implementations • ICCV 2019 • Sai Bi, Kalyan Sunkavalli, Federico Perazzi, Eli Shechtman, Vladimir Kim, Ravi Ramamoorthi
We present a method to improve the visual realism of low-quality, synthetic images, e. g. OpenGL renderings.
no code implementations • 27 May 2019 • Chu Wang, Babak Samari, Vladimir Kim, Siddhartha Chaudhuri, Kaleem Siddiqi
Thus far the learning of attention weights has been driven solely by the minimization of task specific loss functions.
no code implementations • ECCV 2018 • Lingyu Wei, Liwen Hu, Vladimir Kim, Ersin Yumer, Hao Li
To handle the diversity of hairstyles and its appearance complexity, we disentangle hair structure, color, and illumination properties using a sequential GAN architecture and a semi-supervised training approach.
6 code implementations • CVPR 2018 • Samaneh Azadi, Matthew Fisher, Vladimir Kim, Zhaowen Wang, Eli Shechtman, Trevor Darrell
In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.
no code implementations • 28 Oct 2017 • Moos Hueting, Pradyumna Reddy, Vladimir Kim, Ersin Yumer, Nathan Carr, Niloy Mitra
Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc.