Search Results for author: Maria Shugrina

Found 7 papers, 1 papers with code

ATISS: Autoregressive Transformers for Indoor Scene Synthesis

no code implementations NeurIPS 2021 Despoina Paschalidou, Amlan Kar, Maria Shugrina, Karsten Kreis, Andreas Geiger, Sanja Fidler

The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to data synthesis for training and simulation.

Indoor Scene Synthesis

3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations

no code implementations ICCV 2021 Kangxue Yin, Jun Gao, Maria Shugrina, Sameh Khamis, Sanja Fidler

Given a small set of high-quality textured objects, our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation.

3D Reconstruction Data Augmentation +1

UniCon: Universal Neural Controller For Physics-based Character Motion

no code implementations30 Nov 2020 Tingwu Wang, Yunrong Guo, Maria Shugrina, Sanja Fidler

The field of physics-based animation is gaining importance due to the increasing demand for realism in video games and films, and has recently seen wide adoption of data-driven techniques, such as deep reinforcement learning (RL), which learn control from (human) demonstrations.

Neural Turtle Graphics for Modeling City Road Layouts

no code implementations ICCV 2019 Hang Chu, Daiqing Li, David Acuna, Amlan Kar, Maria Shugrina, Xinkai Wei, Ming-Yu Liu, Antonio Torralba, Sanja Fidler

We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts.

Creative Flow+ Dataset

1 code implementation CVPR 2019 Maria Shugrina, Ziheng Liang, Amlan Kar, Jiaman Li, Angad Singh, Karan Singh, Sanja Fidler

We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth.

3D Character Animation From A Single Photo Depth Estimation +7

Color Sails: Discrete-Continuous Palettes for Deep Color Exploration

no code implementations7 Jun 2018 Maria Shugrina, Amlan Kar, Karan Singh, Sanja Fidler

Then, the user can adjust color sail parameters to change the base colors, their blending behavior and the number of colors, exploring a wide range of options for the original design.

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