no code implementations • 6 Oct 2022 • Hassan Abu Alhaija, Alara Dirik, André Knörig, Sanja Fidler, Maria Shugrina
Specifically, we propose a novel method to convert 3D shapes into compact 1-channel geometry images and leverage StyleGAN3 and image-to-image translation networks to generate 3D objects in 2D space.
no code implementations • 2 Dec 2021 • Sourav Biswas, Kangxue Yin, Maria Shugrina, Sanja Fidler, Sameh Khamis
We present HIPNet, a neural implicit pose network trained on multiple subjects across many poses.
1 code implementation • 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.
Ranked #3 on Indoor Scene Synthesis on PRO-teXt
2D Semantic Segmentation task 1 (8 classes) 3D Semantic Scene Completion +1
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.
no code implementations • 30 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.
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.
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
no code implementations • 7 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.