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Creating a physical digital twin of a real-world object has immense potential in robotics, content creation, and XR.
A volumetric method for building complex models from range images
Brian Curless and Marc Levoy · 1996
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The cma evolution strategy: a comparing review
Nikolaus Hansen · 2006
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As-rigid-as-possible surface modeling
Olga Sorkine and Marc Alexa · 2007
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Embedded deformation for shape manipulation
Robert W Sumner, Johannes Schmid, and Mark Pauly · 2007
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Global correspondence optimization for non-rigid registration of depth scans
Hao Li, Robert W Sumner, and Mark Pauly · 2008
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Ep n p: An accurate o (n) solution to the p n p problem
Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua · 2009
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Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time
Richard A Newcombe, Dieter Fox, and Steven M Seitz · 2015
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Deformation capture and modeling of soft objects
Bin Wang, Longhua Wu, KangKang Yin, Uri M Ascher, Libin Liu, and Hui Huang · 2015
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Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Learning to manipulate deformable objects without demonstrations
Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, and Pieter Abbeel · 2019
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Densephysnet: Learning dense physical object representations via multi-step dynamic interactions
Zhenjia Xu, Jiajun Wu, Andy Zeng, Joshua B Tenenbaum, and Shuran Song · 2019
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Add: Analytically differentiable dynamics for multi-body systems with frictional contact
Moritz Geilinger, David Hahn, Jonas Zehnder, Moritz Bächer, Bernhard Thomaszewski, and Stelian Coros · 2020
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Learning mesh-based simulation with graph networks
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter Battaglia · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
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Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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Diffpd: Differentiable projective dynamics
Tao Du, Kui Wu, Pingchuan Ma, Sebastien Wah, Andrew Spielberg, Daniela Rus, and Wojciech Matusik · 2021
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Disect: A differentiable simulation engine for autonomous robotic cutting
Eric Heiden, Miles Macklin, Yashraj Narang, Dieter Fox, Animesh Garg, and Fabio Ramos · 2021
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gradsim: Differentiable simulation for system identification and visuomotor control
Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jérôme Parent-Lévesque, Kevin Xie, Kenny Erleben, et al · 2021
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Neural scene flow fields for space-time view synthesis of dynamic scenes
Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang · 2021
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D-nerf: Neural radiance fields for dynamic scenes
Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer · 2021
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Differentiable simulation of soft multi-body systems
Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, and Ming C. Lin · 2021
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Differentiable implicit soft-body physics
Junior Rojas, Eftychios Sifakis, and Ladislav Kavan · 2021
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Space-time neural irradiance fields for free-viewpoint video
Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim · 2021
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Physics informed neural fields for smoke reconstruction with sparse data
Mengyu Chu, Lingjie Liu, Quan Zheng, Erik Franz, Hans-Peter Seidel, Christian Theobalt, and Rhaleb Zayer · 2022
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Context is everything: Implicit identification for dynamics adaptation
Ben Evans, Abitha Thankaraj, and Lerrel Pinto · 2022
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Monocular dynamic view synthesis: A reality check
Robocook: Long-horizon elasto-plastic object manipulation with diverse tools
Haochen Shi, Huazhe Xu, Samuel Clarke, Yunzhu Li, and Jiajun Wu · 2023
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Flow supervision for deformable nerf
Chaoyang Wang, Lachlan Ewen MacDonald, Laszlo A Jeni, and Simon Lucey · 2023
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Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting
Zeyu Yang, Hongye Yang, Zijie Pan, and Li Zhang · 2023
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Dylin: Making light field networks dynamic
Heng Yu, Joel Julin, Zoltan A Milacski, Koichiro Niinuma, and Laszlo A Jeni · 2023
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4d-rotor gaussian splatting: towards efficient novel view synthesis for dynamic scenes
Yuanxing Duan, Fangyin Wei, Qiyu Dai, Yuhang He, Wenzheng Chen, and Baoquan Chen · 2024
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Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, and Angjoo Kanazawa · 2022
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Neurofluid: Fluid dynamics grounding with particle-driven neural radiance fields
Shanyan Guan, Huayu Deng, Yunbo Wang, and Xiaokang Yang · 2022
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3d neural scene representations for visuomotor control
Yunzhu Li, Shuang Li, Vincent Sitzmann, Pulkit Agrawal, and Antonio Torralba · 2022
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Learning visible connectivity dynamics for cloth smoothing
Xingyu Lin, Yufei Wang, Zixuan Huang, and David Held · 2022
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Pingchuan Ma, Tao Du, Joshua B Tenenbaum, Wojciech Matusik, and Chuang Gan · 2022
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Warp: A high-performance python framework for gpu simulation and graphics
Miles Macklin · 2022
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Cagenerf: Cage-based neural radiance fields for genrenlized 3d deformation and animation
Yicong Peng, Yichao Yan, Shenqi Liu, Yuhao Cheng, Shanyan Guan, Bowen Pan, Guangtao Zhai, and Xiaokang Yang · 2022
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Pie-nerf: Physics-based interactive elastodynamics with nerf
Yutao Feng, Yintong Shang, Xuan Li, Tianjia Shao, Chenfanfu Jiang, and Yin Yang · 2024
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Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes
Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, and Xiaojuan Qi · 2024
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Vr-gs: A physical dynamics-aware interactive gaussian splatting system in virtual reality
Ying Jiang, Chang Yu, Tianyi Xie, Xuan Li, Yutao Feng, Huamin Wang, Minchen Li, Henry Lau, Feng Gao, Yin Yang, et al · 2024
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Cotracker3: Simpler and better point tracking by pseudo-labelling real videos
Nikita Karaev, Iurii Makarov, Jianyuan Wang, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht · 2024
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Dynmf: Neural motion factorization for real-time dynamic view synthesis with 3d gaussian splatting
Agelos Kratimenos, Jiahui Lei, and Kostas Daniilidis · 2024
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Gaussian-flow: 4d reconstruction with dynamic 3d gaussian particle
Youtian Lin, Zuozhuo Dai, Siyu Zhu, and Yao Yao · 2024
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Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis
Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan · 2024
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Grounded sam: Assembling open-world models for diverse visual tasks
Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, et al · 2024
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Robocraft: Learning to see, simulate, and shape elasto-plastic objects in 3d with graph networks
Haochen Shi, Huazhe Xu, Zhiao Huang, Yunzhu Li, and Jiajun Wu · 2024
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4d gaussian splatting for real-time dynamic scene rendering
Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang · 2024
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Structured 3d latents for scalable and versatile 3d generation
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang · 2024
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Physgaussian: Physics-integrated 3d gaussians for generative dynamics
Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, and Chenfanfu Jiang · 2024
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Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction
Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin · 2024
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Cogs: Controllable gaussian splatting
Heng Yu, Joel Julin, Zoltán Á Milacski, Koichiro Niinuma, and László A Jeni · 2024
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Reconstruction and simulation of elastic objects with spring-mass 3d gaussians
Licheng Zhong, Hong-Xing Yu, Jiajun Wu, and Yunzhu Li · 2024
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