| Training deep networks with synthetic data: Bridging the reality gap by domain randomization J Tremblay, A Prakash, D Acuna, M Brophy, V Jampani, C Anil, T To, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 405 | 2018 |
| Deep object pose estimation for semantic robotic grasping of household objects J Tremblay, T To, B Sundaralingam, Y Xiang, D Fox, S Birchfield arXiv preprint arXiv:1809.10790, 2018 | 290 | 2018 |
| Pamtri: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data Z Tang, M Naphade, S Birchfield, J Tremblay, W Hodge, R Kumar, ... Proceedings of the IEEE/CVF International Conference on Computer Vision, 211-220, 2019 | 84 | 2019 |
| Falling things: A synthetic dataset for 3d object detection and pose estimation J Tremblay, T To, S Birchfield Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 72 | 2018 |
| An exploration tool for predicting stealthy behaviour J Tremblay, PA Torres, N Rikovitch, C Verbrugge Ninth Artificial Intelligence and Interactive Digital Entertainment Conference, 2013 | 41 | 2013 |
| Synthetically trained neural networks for learning human-readable plans from real-world demonstrations J Tremblay, T To, A Molchanov, S Tyree, J Kautz, S Birchfield 2018 IEEE International Conference on Robotics and Automation (ICRA), 5659-5666, 2018 | 37 | 2018 |
| Measuring Risk in Stealth Games J Tremblay, PA Torres, C Verbrugge FDG, 2014 | 26 | 2014 |
| Guided uncertainty-aware policy optimization: Combining learning and model-based strategies for sample-efficient policy learning MA Lee, C Florensa, J Tremblay, N Ratliff, A Garg, F Ramos, D Fox 2020 IEEE International Conference on Robotics and Automation (ICRA), 7505-7512, 2020 | 23 | 2020 |
| NDDS: NVIDIA deep learning dataset synthesizer, 2018 T To, J Tremblay, D McKay, Y Yamaguchi, K Leung, A Balanon, J Cheng, ... | 22 | |
| NDDS: NVIDIA deep learning dataset synthesizer T To, J Tremblay, D McKay, Y Yamaguchi, K Leung, A Balanon, J Cheng, ... | 20 | 2018 |
| Adaptive Game Mechanics for Learning Purposes-Making Serious Games Playable and Fun. J Tremblay, B Bouchard, A Bouzouane CSEDU (2), 465-470, 2010 | 20 | 2010 |
| Camera-to-robot pose estimation from a single image TE Lee, J Tremblay, T To, J Cheng, T Mosier, O Kroemer, D Fox, ... 2020 IEEE International Conference on Robotics and Automation (ICRA), 9426-9432, 2020 | 19 | 2020 |
| Adaptive Companions in FPS Games J Tremblay, C Verbrugge 8th International Conference on Foundations of Digital Games (FDG), 229-236, 2013 | 18 | 2013 |
| Generative methods for guard and camera placement in stealth games Q Xu, J Tremblay, C Verbrugge Proceedings of the AAAI Conference on Artificial Intelligence and …, 2014 | 17 | 2014 |
| I can jump! exploring search algorithms for simulating platformer players J Tremblay, A Borodovski, C Verbrugge Proceedings of the AAAI Conference on Artificial Intelligence and …, 2014 | 15 | 2014 |
| Target selection for AI companions in FPS games. J Tremblay, C Dragert, C Verbrugge FDG, 2014 | 15 | 2014 |
| An Algorithmic Approach to Analyzing Combat and Stealth Games J Tremblay, PA Torres, C Verbrugge CIG, 2014 | 15 | 2014 |
| Few-shot viewpoint estimation HY Tseng, S De Mello, J Tremblay, S Liu, S Birchfield, MH Yang, J Kautz arXiv preprint arXiv:1905.04957, 2019 | 12 | 2019 |
| Clustering Player Paths. J Campbell, J Tremblay, C Verbrugge FDG, 2015 | 11 | 2015 |
| Understanding and evaluating behaviour trees J Tremblay McGill University, Modelling, Simulation and Design Lab, Tech. Rep, 2012 | 11 | 2012 |
Stan BirchfieldNVIDIA; Clemson UniversityVerified email at clemson.edu
