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We present a new test-time optimization method for estimating dense and long-range motion from a video sequence.
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Noah Snavely, Steven M Seitz, and Richard Szeliski · 2008
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Large displacement optical flow
Thomas Brox, Christoph Bregler, and Jitendra Malik · 2009
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Vijay Badrinarayanan, Fabio Galasso, and Roberto Cipolla · 2010
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Large displacement optical flow: descriptor matching in variational motion estimation
Thomas Brox and Jitendra Malik · 2010
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Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
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Deqing Sun, Erik Sudderth, and Michael Black · 2010
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Narayanan Sundaram, Thomas Brox, and Kurt Keutzer · 2010
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Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski · 2011
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José Lezama, Karteek Alahari, Josef Sivic, and Ivan Laptev · 2011
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Modeling temporal coherence for optical flow
Sebastian Volz, Andres Bruhn, Levi Valgaerts, and Henning Zimmer · 2011
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Towards longer long-range motion trajectories
Michael Rubinstein, Ce Liu, and William T Freeman · 2012
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Jason Chang, Donglai Wei, and John W Fisher · 2013
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Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, and Cordelia Schmid · 2013
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Diederik P Kingma and Jimmy Ba · 2014
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Flownet: Learning optical flow with convolutional networks
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
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Learning dense correspondence via 3d-guided cycle consistency
Tinghui Zhou, Philipp Krahenbuhl, Mathieu Aubry, Qixing Huang, and Alexei A Efros · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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The 2017 DAVIS challenge on video object segmentation
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NeRF: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
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Nerfies: Deformable neural radiance fields
Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla · 2021
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HyperNeRF: A higher-dimensional representation for topologically varying neural radiance fields
Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, and Steven M. Seitz · 2021
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Neural parts: Learning expressive 3d shape abstractions with invertible neural networks
Despoina Paschalidou, Angelos Katharopoulos, Andreas Geiger, and Sanja Fidler · 2021
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Loftr: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
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Liteflownet: A lightweight convolutional neural network for optical flow estimation
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy · 2018
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Unsupervised learning of multi-frame optical flow with occlusions
Joel Janai, Fatma Guney, Anurag Ranjan, Michael Black, and Andreas Geiger · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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Neighbourhood consensus networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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Tracking emerges by colorizing videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, and Kevin Murphy · 2018
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D2-net: A trainable cnn for joint description and detection of local features
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Learning accurate dense correspondences and when to trust them
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Space-time neural irradiance fields for free-viewpoint video
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Rethinking self-supervised correspondence learning: A video frame-level similarity perspective
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Lasr: Learning articulated shape reconstruction from a monocular video
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mip-NeRF 360: Unbounded anti-aliased neural radiance fields
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Learning pixel trajectories with multiscale contrastive random walks
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Neural surface reconstruction of dynamic scenes with monocular rgb-d camera
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Tap-vid: A benchmark for tracking any point in a video
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Monocular dynamic view synthesis: A reality check
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Kubric: A scalable dataset generator
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Particle video revisited: Tracking through occlusions using point trajectories
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Cadex: Learning canonical deformation coordinate space for dynamic surface representation via neural homeomorphism
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Dynibar: Neural dynamic image-based rendering
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Instant neural graphics primitives with a multiresolution hash encoding
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Block-nerf: Scalable large scene neural view synthesis
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Casa: Category-agnostic skeletal animal reconstruction
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Deformable sprites for unsupervised video decomposition
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ParticleSfM: Exploiting dense point trajectories for localizing moving cameras in the wild
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Tapir: Tracking any point with per-frame initialization and temporal refinement
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Cotracker: It is better to track together
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Mft: Long-term tracking of every pixel
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