{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:24:38Z","timestamp":1765268678295,"version":"3.28.0"},"reference-count":60,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1109\/wacv45572.2020.9093374","type":"proceedings-article","created":{"date-parts":[[2020,5,15]],"date-time":"2020-05-15T03:41:09Z","timestamp":1589514069000},"page":"2823-2832","source":"Crossref","is-referenced-by-count":4,"title":["Predicting the Physical Dynamics of Unseen 3D Objects"],"prefix":"10.1109","author":[{"given":"Davis","family":"Rempe","sequence":"first","affiliation":[]},{"given":"Srinath","family":"Sridhar","sequence":"additional","affiliation":[]},{"given":"He","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Leonidas J.","family":"Guibas","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Flexible neural representation for physics prediction","author":"mrowca","year":"2018","journal-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS)"},{"article-title":"what happens if…","year":"0","author":"mottaghi","key":"ref38"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_1"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793509"},{"key":"ref31","article-title":"Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids","author":"li","year":"2019","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989304"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.383"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2982421"},{"key":"ref35","article-title":"Generalizable Features From Unsupervised Learning","author":"mirza","year":"2016","journal-title":"arXiv preprint arXiv 1612 03809"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601152"},{"article-title":"Unsupervised learning of latent physical properties using perception-prediction networks","year":"0","author":"zheng","key":"ref60"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1068\/p110173"},{"key":"ref27","first-page":"430","article-title":"Learning physical intuition of block towers by example","author":"lerer","year":"2016","journal-title":"Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML)"},{"key":"ref29","article-title":"To fall or not to fall: A visual approach to physical stability prediction","author":"li","year":"2016","journal-title":"arXiv preprint arXiv 1604 00066"},{"journal-title":"Unity Game Engine","year":"0","key":"ref2"},{"journal-title":"Bullet Physics Engine","year":"0","key":"ref1"},{"journal-title":"NeuroAnimator fast neural network emulation and control of physics-based models","year":"2000","author":"grzeszczuk","key":"ref20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794333"},{"key":"ref21","article-title":"Imagination-based decision making with physical models in deep neural networks","author":"hamrick","year":"2016","journal-title":"Advances in Neural Information Processing Systems (NIPS) Intuitive Physics Workshop"},{"key":"ref24","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc of the Int Conf on Learning Representations (ICLR)"},{"key":"ref23","article-title":"Reasoning about physical interactions with object-oriented prediction and planning","author":"janner","year":"2019","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2017.06.002"},{"key":"ref25","article-title":"Neural relational inference for interacting systems","author":"kipf","year":"2018","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/688"},{"key":"ref51","article-title":"Visual interaction networks","author":"watters","year":"2017","journal-title":"arXiv preprint arXiv 1706 01433"},{"key":"ref59","article-title":"A comparative evaluation of approximate probabilistic simulation and deep neural networks as accounts of human physical scene understanding","author":"zhang","year":"2016","journal-title":"Annual Meeting of the Cognitive Science Society"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7758091"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_6"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2019.XV.046"},{"key":"ref55","article-title":"3d shapenets: A deep representation for volumetric shapes","author":"wu","year":"2015","journal-title":"Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref54","first-page":"127","article-title":"Galileo: Perceiving physical object properties by integrating a physics engine with deep learning","author":"wu","year":"2015","journal-title":"Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref53","article-title":"Learning to see physics via visual de-animation","author":"wu","year":"2017","journal-title":"proceedings of the 31st Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.39"},{"key":"ref10","article-title":"Shapenet: An information-rich 3d model repository","author":"chang","year":"2015","journal-title":"arXiv preprint arXiv 1512 00327"},{"key":"ref11","article-title":"A compositional object-based approach to learning physical dynamics","author":"chang","year":"2017","journal-title":"Proc of the Int Conf on Learning Representations (ICLR)"},{"key":"ref40","article-title":"Action-conditional video prediction using deep networks in atari games","author":"oh","year":"2015","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"ref12","article-title":"End-to-end differentiable physics for learning and control","author":"de avila belbute-peres","year":"2018","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref13","article-title":"Learning A Physical Long-term Predictor","author":"ehrhardt","year":"2017","journal-title":"arXiv preprint arXiv 1703 08337"},{"key":"ref14","article-title":"Unsupervised intuitive physics from visual observations","author":"ehrhardt","year":"2018","journal-title":"arXiv preprint arXiv 1805 05086"},{"key":"ref15","article-title":"Learning to Represent Mechanics via Long-term Extrapolation and Interpolation","author":"ehrhardt","year":"2017","journal-title":"arXiv preprint arXiv 1706 02179"},{"key":"ref16","first-page":"64","article-title":"Unsupervised learning for physical interaction through video prediction","author":"finn","year":"2016","journal-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989324"},{"key":"ref18","article-title":"A disentangled recognition and nonlinear dynamics model for unsupervised learning","author":"fraccaro","year":"2017","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"ref19","article-title":"Learning visual predictive models of physics for playing billiards","author":"fragkiadaki","year":"2016","journal-title":"Proceedings of the 4th International Conference on Learning Representations (ICLR)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593995"},{"key":"ref3","article-title":"Learning to poke by poking: Experiential learning of intuitive physics","author":"agrawal","year":"2016","journal-title":"Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref6","first-page":"4509","article-title":"Interaction networks for learning about objects, relations and physics","author":"battaglia","year":"2016","journal-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/0885-2014(90)90011-H"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989023"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1306572110"},{"key":"ref49","article-title":"Relational neural expectation maximization: Unsupervised discovery of objects and their interactions","author":"van steenkiste","year":"2018","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461184"},{"key":"ref46","article-title":"Modeling expectation violation in intuitive physics with coarse probabilistic object representations","author":"smith","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref45","article-title":"Spnets: Differentiable fluid dynamics for deep neural networks","author":"schenck","year":"2018","journal-title":"Conference on Robot Learning (CoRL)"},{"key":"ref48","first-page":"1017","article-title":"Generating text with recurrent neural networks","author":"sutskever","year":"2011","journal-title":"Proceedings of the 28th International Conference on Machine Learning (ICML-11)"},{"key":"ref47","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v31i1.10934","article-title":"Label-free supervision of neural networks with physics and domain knowledge","author":"stewart","year":"2017","journal-title":"Proc of AAAI Conference on Artificial Intelligence"},{"key":"ref42","article-title":"Learning generalizable physical dynamics of 3d rigid objects","author":"rempe","year":"2019","journal-title":"arXiv preprint arXiv 1901 00466"},{"key":"ref41","first-page":"4","article-title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","volume":"1","author":"qi","year":"2017","journal-title":"Proc Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref44","article-title":"Graph networks as learnable physics engines for inference and control","author":"sanchez-gonzalez","year":"2018","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"key":"ref43","article-title":"Intphys: A framework and benchmark for visual intuitive physics reasoning","author":"riochet","year":"2018","journal-title":"arXiv preprint arXiv 1803 07616"}],"event":{"name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","start":{"date-parts":[[2020,3,1]]},"location":"Snowmass Village, CO, USA","end":{"date-parts":[[2020,3,5]]}},"container-title":["2020 IEEE Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9087828\/9093261\/09093374.pdf?arnumber=9093374","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T19:43:31Z","timestamp":1666554211000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9093374\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/wacv45572.2020.9093374","relation":{},"subject":[],"published":{"date-parts":[[2020,3]]}}}