Meta AI (FAIR) · embodied AI & robot learningMeta AI(FAIR) · 具身智能与机器人学习
31 curated papers, code, datasets and hardware from Meta AI (FAIR) (22 with code), with one-line takeaways.31 个来自Meta AI(FAIR)的精选论文、代码、数据集与硬件(22 个附代码),均配一句话要点。
Labs are matched from each entry's affiliation field by simple name rules (data/labs.yaml); joint work appears under every lab named. Only curated entries are listed, because daily arXiv papers carry no affiliations.机构由每个条目的机构字段按名称规则匹配(data/labs.yaml),合作工作会出现在所有署名机构下。仅列出精选条目,因为每日 arXiv 论文不含机构信息。
SAM — a promptable segmentation model (points, boxes, masks) trained on 1B+ masks; a standard building block for object masks in robot perception pipelines.SAM:可用点、框、掩码提示的分割模型,在 10 亿级掩码上训练;已成为机器人感知流程中获取物体掩码的常用基础组件。
@misc{kirillov2023segment,
title = {{Segment Anything}},
author = {Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
year = {2023},
eprint = {2304.02643},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2304.02643},
}
@misc{lambeta2020digit,
title = {{DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation}},
author = {Mike Lambeta and Po-Wei Chou and Stephen Tian and Brian Yang and Benjamin Maloon and Victoria Rose Most and Dave Stroud and Raymond Santos and Ahmad Byagowi and Gregg Kammerer and Dinesh Jayaraman and Roberto Calandra},
year = {2020},
eprint = {2005.14679},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2005.14679},
}
Segments and tracks every instance of a concept given a short noun phrase or image exemplar, in images and video; trained with a data engine covering 4M concept labels, with open weights and the SA-Co benchmark.给定短名词短语或示例图像,即可在图像和视频中分割并跟踪某一概念的所有实例;数据引擎覆盖 400 万个概念标签,开放权重与 SA-Co 基准,可直接用作机器人开放词汇感知模块。
@misc{carion2025sam,
title = {{SAM 3: Segment Anything with Concepts}},
author = {Nicolas Carion and Laura Gustafson and Yuan-Ting Hu and Shoubhik Debnath and Ronghang Hu and Didac Suris and Chaitanya Ryali and Kalyan Vasudev Alwala and Haitham Khedr and Andrew Huang and Jie Lei and Tengyu Ma and Baishan Guo and Arpit Kalla and Markus Marks and Joseph Greer and Meng Wang and Peize Sun and Roman Rädle and Triantafyllos Afouras and Effrosyni Mavroudi and Katherine Xu and Tsung-Han Wu and Yu Zhou and Liliane Momeni and Rishi Hazra and Shuangrui Ding and Sagar Vaze and Francois Porcher and Feng Li and Siyuan Li and Aishwarya Kamath and Ho Kei Cheng and Piotr Dollár and Nikhila Ravi and Kate Saenko and Pengchuan Zhang and Christoph Feichtenhofer},
year = {2025},
eprint = {2511.16719},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2511.16719},
}
A self-supervised video encoder, post-trained with a little robot data, plans zero-shot pick-and-place by latent prediction.自监督视频编码器经少量机器人数据后训练,通过潜空间预测实现零样本抓放规划。
@misc{assran2025v,
title = {{V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning}},
author = {Mido Assran and Adrien Bardes and David Fan and Quentin Garrido and Russell Howes and Mojtaba and Komeili and Matthew Muckley and Ammar Rizvi and Claire Roberts and Koustuv Sinha and Artem Zholus and Sergio Arnaud and Abha Gejji and Ada Martin and Francois Robert Hogan and Daniel Dugas and Piotr Bojanowski and Vasil Khalidov and Patrick Labatut and Francisco Massa and Marc Szafraniec and Kapil Krishnakumar and Yong Li and Xiaodong Ma and Sarath Chandar and Franziska Meier and Yann LeCun and Michael Rabbat and Nicolas Ballas},
year = {2025},
eprint = {2506.09985},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2506.09985},
}
One feed-forward transformer predicts cameras, depth, point maps and tracks for many views at once, in about a second.单个前馈 Transformer 一次性为多视角图像预测相机参数、深度、点图与跟踪,约一秒完成。
@misc{wang2025vggt,
title = {{VGGT: Visual Geometry Grounded Transformer}},
author = {Jianyuan Wang and Minghao Chen and Nikita Karaev and Andrea Vedaldi and Christian Rupprecht and David Novotny},
year = {2025},
eprint = {2503.11651},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2503.11651},
}
@misc{bhirangi2024anyskin,
title = {{AnySkin: Plug-and-play Skin Sensing for Robotic Touch}},
author = {Raunaq Bhirangi and Venkatesh Pattabiraman and Enes Erciyes and Yifeng Cao and Tess Hellebrekers and Lerrel Pinto},
year = {2024},
eprint = {2409.08276},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.08276},
}
Digit 360: an open-design, finger-shaped sensor that senses force, fine texture, vibration and heat, with an on-board neural accelerator for reflex-like local processing.Digit 360:开放设计的指尖形触觉传感器,可感知力、细微纹理、振动和温度,并用板载神经网络加速器做类似反射弧的本地处理。
@misc{lambeta2024digitizing,
title = {{Digitizing Touch with an Artificial Multimodal Fingertip}},
author = {Mike Lambeta and Tingfan Wu and Ali Sengul and Victoria Rose Most and Nolan Black and Kevin Sawyer and Romeo Mercado and Haozhi Qi and Alexander Sohn and Byron Taylor and Norb Tydingco and Gregg Kammerer and Dave Stroud and Jake Khatha and Kurt Jenkins and Kyle Most and Neal Stein and Ricardo Chavira and Thomas Craven-Bartle and Eric Sanchez and Yitian Ding and Jitendra Malik and Roberto Calandra},
year = {2024},
eprint = {2411.02479},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.02479},
}
@misc{liu2024ok,
title = {{OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics}},
author = {Peiqi Liu and Yaswanth Orru and Jay Vakil and Chris Paxton and Nur Muhammad Mahi Shafiullah and Lerrel Pinto},
year = {2024},
eprint = {2401.12202},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.12202},
}
Extends SAM to video with a streaming memory, so a mask prompted once is tracked across frames — handy for following objects during manipulation.用流式记忆把 SAM 扩展到视频:一次提示得到的掩码可在后续帧中持续跟踪,适合在操作过程中跟随目标物体。
@misc{ravi2024sam,
title = {{SAM 2: Segment Anything in Images and Videos}},
author = {Nikhila Ravi and Valentin Gabeur and Yuan-Ting Hu and Ronghang Hu and Chaitanya Ryali and Tengyu Ma and Haitham Khedr and Roman Rädle and Chloe Rolland and Laura Gustafson and Eric Mintun and Junting Pan and Kalyan Vasudev Alwala and Nicolas Carion and Chao-Yuan Wu and Ross Girshick and Piotr Dollár and Christoph Feichtenhofer},
year = {2024},
eprint = {2408.00714},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2408.00714},
}
@misc{higuera2024sparsh,
title = {{Sparsh: Self-supervised touch representations for vision-based tactile sensing}},
author = {Carolina Higuera and Akash Sharma and Chaithanya Krishna Bodduluri and Taosha Fan and Patrick Lancaster and Mrinal Kalakrishnan and Michael Kaess and Byron Boots and Mike Lambeta and Tingfan Wu and Mustafa Mukadam},
year = {2024},
eprint = {2410.24090},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24090},
}
Transformer that tracks many video points jointly rather than one at a time, staying robust through occlusions; widely used to extract motion tracks from human or robot videos for policy learning.联合跟踪视频中大量点(而非逐点独立跟踪)的 Transformer,遮挡下依然稳健;常用于从人类或机器人视频中提取运动轨迹来学习策略。
@misc{karaev2023cotracker,
title = {{CoTracker: It is Better to Track Together}},
author = {Nikita Karaev and Ignacio Rocco and Benjamin Graham and Natalia Neverova and Andrea Vedaldi and Christian Rupprecht},
year = {2023},
eprint = {2307.07635},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2307.07635},
}
Self-supervised image features that work frozen for depth, segmentation and retrieval; a common visual backbone in VLAs (e.g. OpenVLA) and latent world models.自监督学习的图像特征,冻结即可用于深度估计、分割和检索;是 VLA(如 OpenVLA)和潜空间世界模型常用的视觉骨干。
@misc{oquab2023dinov,
title = {{DINOv2: Learning Robust Visual Features without Supervision}},
author = {Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
year = {2023},
eprint = {2304.07193},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2304.07193},
}
Time-synced first-person and third-person video of people performing skilled activities (cooking, sports, repair...), with rich annotations; a large source for learning manipulation skills from humans.人们进行技能活动(烹饪、运动、维修等)时同步采集的第一人称与第三人称视频,附丰富标注,是从人类视频学习操作技能的重要数据来源。
@misc{grauman2023ego,
title = {{Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives}},
author = {Kristen Grauman and Andrew Westbury and Lorenzo Torresani and Kris Kitani and Jitendra Malik and Triantafyllos Afouras and Kumar Ashutosh and Vijay Baiyya and Siddhant Bansal and Bikram Boote and Eugene Byrne and Zach Chavis and Joya Chen and Feng Cheng and Fu-Jen Chu and Sean Crane and Avijit Dasgupta and Jing Dong and Maria Escobar and Cristhian Forigua and Abrham Gebreselasie and Sanjay Haresh and Jing Huang and Md Mohaiminul Islam and Suyog Jain and Rawal Khirodkar and Devansh Kukreja and Kevin J Liang and Jia-Wei Liu and Sagnik Majumder and Yongsen Mao and Miguel Martin and Effrosyni Mavroudi and Tushar Nagarajan and Francesco Ragusa and Santhosh Kumar Ramakrishnan and Luigi Seminara and Arjun Somayazulu and Yale Song and Shan Su and Zihui Xue and Edward Zhang and Jinxu Zhang and Angela Castillo and Changan Chen and Xinzhu Fu and Ryosuke Furuta and Cristina Gonzalez and Prince Gupta and Jiabo Hu and others},
year = {2023},
eprint = {2311.18259},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2311.18259},
}
RotateIt trains in simulation with privileged object information, then distills to a policy that rotates diverse objects in hand using vision, touch and proprioception on a real hand.RotateIt 先在仿真中利用特权物体信息训练,再蒸馏为仅用视觉、触觉和本体感觉的策略,在真实灵巧手上旋转多种物体。
@misc{qi2023general,
title = {{General In-Hand Object Rotation with Vision and Touch}},
author = {Haozhi Qi and Brent Yi and Sudharshan Suresh and Mike Lambeta and Yi Ma and Roberto Calandra and Jitendra Malik},
year = {2023},
eprint = {2309.09979},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.09979},
}
Adds simulated humanoid avatars and a human-in-the-loop interface to Habitat, so robots can be trained and evaluated on tasks shared with people, such as social navigation and social rearrangement.为 Habitat 加入仿真人形化身和真人在环交互接口,可在与人协作的任务(如社交导航、社交重排)上训练和评测机器人。
@misc{puig2023habitat,
title = {{Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots}},
author = {Xavier Puig and Eric Undersander and Andrew Szot and Mikael Dallaire Cote and Tsung-Yen Yang and Ruslan Partsey and Ruta Desai and Alexander William Clegg and Michal Hlavac and So Yeon Min and Vladimír Vondruš and Theophile Gervet and Vincent-Pierre Berges and John M. Turner and Oleksandr Maksymets and Zsolt Kira and Mrinal Kalakrishnan and Jitendra Malik and Devendra Singh Chaplot and Unnat Jain and Dhruv Batra and Akshara Rai and Roozbeh Mottaghi},
year = {2023},
eprint = {2310.13724},
archivePrefix = {arXiv},
primaryClass = {cs.HC},
url = {https://arxiv.org/abs/2310.13724},
}
NeuralFeels fuses vision and touch from a multi-fingered hand into an online neural field, reconstructing an object's shape and tracking its pose while the hand manipulates it.NeuralFeels 将多指手的视觉与触觉在线融合为神经场,在操作过程中同时重建物体形状并跟踪其位姿。
@misc{suresh2023neural,
title = {{Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation}},
author = {Sudharshan Suresh and Haozhi Qi and Tingfan Wu and Taosha Fan and Luis Pineda and Mike Lambeta and Jitendra Malik and Mrinal Kalakrishnan and Roberto Calandra and Michael Kaess and Joseph Ortiz and Mustafa Mukadam},
year = {2023},
eprint = {2312.13469},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.13469},
}
Dobb-E collects home demonstrations with a cheap reacher-grabber "stick", pretrains on them, then adapts to a new household task from a few minutes of data.Dobb-E 用低成本的手持夹取“棒”采集家庭示范数据并预训练,在新家庭任务上只需几分钟数据即可适配。
@misc{shafiullah2023bringing,
title = {{On Bringing Robots Home}},
author = {Nur Muhammad Mahi Shafiullah and Anant Rai and Haritheja Etukuru and Yiqian Liu and Ishan Misra and Soumith Chintala and Lerrel Pinto},
year = {2023},
eprint = {2311.16098},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2311.16098},
}
PIRLNav — behavior cloning on human ObjectNav demonstrations, then RL fine-tuning of the same policy; the paper reports 65.0% success on Habitat ObjectNav, 5 points above the previous best.PIRLNav:先在人类 ObjectNav 演示上做行为克隆,再用强化学习微调同一策略;论文报告在 Habitat ObjectNav 上成功率 65.0%,比此前最佳高 5 个百分点。
@misc{ramrakhya2023pirlnav,
title = {{PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav}},
author = {Ram Ramrakhya and Dhruv Batra and Erik Wijmans and Abhishek Das},
year = {2023},
eprint = {2301.07302},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2301.07302},
}
@misc{nair2022r,
title = {{R3M: A Universal Visual Representation for Robot Manipulation}},
author = {Suraj Nair and Aravind Rajeswaran and Vikash Kumar and Chelsea Finn and Abhinav Gupta},
year = {2022},
eprint = {2203.12601},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2203.12601},
}
Recasts RL as sequence modeling — a GPT-style model predicts actions conditioned on the desired return; an early bridge between transformers and control.把强化学习改写成序列建模:GPT 式模型以期望回报为条件预测动作;是 Transformer 与控制结合的早期代表。
@misc{chen2021decision,
title = {{Decision Transformer: Reinforcement Learning via Sequence Modeling}},
author = {Lili Chen and Kevin Lu and Aravind Rajeswaran and Kimin Lee and Aditya Grover and Michael Laskin and Pieter Abbeel and Aravind Srinivas and Igor Mordatch},
year = {2021},
eprint = {2106.01345},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2106.01345},
}
@misc{grauman2021ego,
title = {{Ego4D: Around the World in 3,000 Hours of Egocentric Video}},
author = {Kristen Grauman and Andrew Westbury and Eugene Byrne and Zachary Chavis and Antonino Furnari and Rohit Girdhar and Jackson Hamburger and Hao Jiang and Miao Liu and Xingyu Liu and Miguel Martin and Tushar Nagarajan and Ilija Radosavovic and Santhosh Kumar Ramakrishnan and Fiona Ryan and Jayant Sharma and Michael Wray and Mengmeng Xu and Eric Zhongcong Xu and Chen Zhao and Siddhant Bansal and Dhruv Batra and Vincent Cartillier and Sean Crane and Tien Do and Morrie Doulaty and Akshay Erapalli and Christoph Feichtenhofer and Adriano Fragomeni and Qichen Fu and Abrham Gebreselasie and Cristina Gonzalez and James Hillis and Xuhua Huang and Yifei Huang and Wenqi Jia and Weslie Khoo and Jachym Kolar and Satwik Kottur and Anurag Kumar and Federico Landini and Chao Li and Yanghao Li and Zhenqiang Li and Karttikeya Mangalam and Raghava Modhugu and Jonathan Munro and Tullie Murrell and Takumi Nishiyasu and Will Price and others},
year = {2021},
eprint = {2110.07058},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2110.07058},
}
Adds fast rigid-body physics and an interactive apartment dataset (ReplicaCAD) to Habitat, plus a home-rearrangement benchmark for mobile manipulators.为 Habitat 加入快速刚体物理与可交互公寓数据集 ReplicaCAD,并提出面向移动操作机器人的家居物品重排基准。
@misc{szot2021habitat,
title = {{Habitat 2.0: Training Home Assistants to Rearrange their Habitat}},
author = {Andrew Szot and Alex Clegg and Eric Undersander and Erik Wijmans and Yili Zhao and John Turner and Noah Maestre and Mustafa Mukadam and Devendra Chaplot and Oleksandr Maksymets and Aaron Gokaslan and Vladimir Vondrus and Sameer Dharur and Franziska Meier and Wojciech Galuba and Angel Chang and Zsolt Kira and Vladlen Koltun and Jitendra Malik and Manolis Savva and Dhruv Batra},
year = {2021},
eprint = {2106.14405},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2106.14405},
}
1,000 3D scans of real indoor spaces for Habitat; the main scene set behind ObjectNav and many embodied navigation benchmarks.1000 个真实室内空间的 3D 扫描,供 Habitat 使用;ObjectNav 等众多具身导航基准的主要场景集。
@misc{ramakrishnan2021habitat,
title = {{Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI}},
author = {Santhosh K. Ramakrishnan and Aaron Gokaslan and Erik Wijmans and Oleksandr Maksymets and Alex Clegg and John Turner and Eric Undersander and Wojciech Galuba and Andrew Westbury and Angel X. Chang and Manolis Savva and Yili Zhao and Dhruv Batra},
year = {2021},
eprint = {2109.08238},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2109.08238},
}
DrQ-v2 - a simple model-free actor-critic with image augmentation that learns continuous control from pixels (incl. humanoid tasks in the DeepMind Control Suite) on a single GPU; a common visual-RL baseline.DrQ-v2:带图像增强的简单无模型 actor-critic,单 GPU 即可从像素学习连续控制(包括 DeepMind Control Suite 的人形任务),是常用的视觉强化学习基线。
@misc{kumar2021rma,
title = {{RMA: Rapid Motor Adaptation for Legged Robots}},
author = {Ashish Kumar and Zipeng Fu and Deepak Pathak and Jitendra Malik},
year = {2021},
eprint = {2107.04034},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2107.04034},
}
A thin magnetic-elastomer skin read by magnetometers; cheap to make and swap, with learned calibration that carries over to new skins.由磁力计读取的薄磁性弹性体皮肤,制作和更换成本低,学习得到的标定可迁移到新皮肤上。
VLN-CE moves Room-to-Room instructions from a fixed navigation graph into continuous Habitat scenes with low-level actions; the standard setting for realistic VLN.VLN-CE 把 Room-to-Room 指令从固定导航图搬到 Habitat 连续环境中、使用底层动作执行,是更贴近真实的 VLN 标准设定。
@misc{krantz2020beyond,
title = {{Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments}},
author = {Jacob Krantz and Erik Wijmans and Arjun Majumdar and Dhruv Batra and Stefan Lee},
year = {2020},
eprint = {2004.02857},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2004.02857},
}
Splits exploration into a learned neural SLAM module, a global policy that chooses long-term goals and a local policy that reaches them, which is far more sample-efficient than end-to-end RL.将探索拆为可学习的神经 SLAM 模块、选择长期目标的全局策略和到达目标的局部策略,比端到端强化学习样本效率高得多。
@misc{chaplot2020learning,
title = {{Learning to Explore using Active Neural SLAM}},
author = {Devendra Singh Chaplot and Dhiraj Gandhi and Saurabh Gupta and Abhinav Gupta and Ruslan Salakhutdinov},
year = {2020},
eprint = {2004.05155},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2004.05155},
}
SemExp builds an episodic semantic map and learns a policy that picks long-term goals on it, a modular recipe that became a standard ObjectNav baseline in Habitat.SemExp 构建情景语义地图,并学习在地图上选择长期目标的策略;这一模块化方案成为 Habitat 中 ObjectNav 的常用基线。
@misc{wang2020tacto,
title = {{TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors}},
author = {Shaoxiong Wang and Mike Lambeta and Po-Wei Chou and Roberto Calandra},
year = {2020},
eprint = {2012.08456},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2012.08456},
}
@misc{savva2019habitat,
title = {{Habitat: A Platform for Embodied AI Research}},
author = {Manolis Savva and Abhishek Kadian and Oleksandr Maksymets and Yili Zhao and Erik Wijmans and Bhavana Jain and Julian Straub and Jia Liu and Vladlen Koltun and Jitendra Malik and Devi Parikh and Dhruv Batra},
year = {2019},
eprint = {1904.01201},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/1904.01201},
}