Search Results for author: Vladimir Kim

Found 9 papers, 4 papers with code

The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts

no code implementations1 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.

Neural Surface Maps

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.

Modeling Artistic Workflows for Image Generation and Editing

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.

Image Generation

FAN: Focused Attention Networks

no code implementations27 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.

Document Classification Object Detection +1

Real-Time Hair Rendering using Sequential Adversarial Networks

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.

Multi-Content GAN for Few-Shot Font Style Transfer

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.

Font Style Transfer

SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images

no code implementations28 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.

object-detection Object Detection

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