-
2
-
-
70349655522
-
Patchmatch: A randomized correspondence algorithm for structural image editing
-
5, 8
-
C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman. Patchmatch: A randomized correspondence algorithm for structural image editing. ACM Transactions on Computer Graphics (TOG), 2009. 5, 8
-
(2009)
ACM Transactions On Computer Graphics (TOG)
-
-
Barnes, C.1
Shechtman, E.2
Finkelstein, A.3
Goldman, D.B.4
-
4
-
-
85006694626
-
-
arXiv:1512.03012, 2, 6
-
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu. ShapeNet: An Information-Rich 3D Model Repository. arXiv:1512.03012, 2015. 2, 6
-
(2015)
ShapeNet: An Information-Rich 3D Model Repository
-
-
Chang, A.X.1
Funkhouser, T.2
Guibas, L.3
Hanrahan, P.4
Huang, Q.5
Li, Z.6
Savarese, S.7
Savva, M.8
Song, S.9
Su, H.10
Xiao, J.11
Yi, L.12
Yu, F.13
-
5
-
-
85018862711
-
3dr2n2: A unified approach for single and multi-view 3d object reconstruction
-
7
-
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese. 3dr2n2: A unified approach for single and multi-view 3d object reconstruction. In ECCV, 2016. 7
-
(2016)
ECCV
-
-
Choy, C.B.1
Xu, D.2
Gwak, J.3
Chen, K.4
Savarese, S.5
-
6
-
-
85019269786
-
Generating images with perceptual similarity metrics based on deep networks
-
2, 5
-
A. Dosovitskiy and T. Brox. Generating images with perceptual similarity metrics based on deep networks. In NIPS, 2016. 2, 5
-
(2016)
NIPS
-
-
Dosovitskiy, A.1
Brox, T.2
-
7
-
-
84973904859
-
Flownet: Learning optical flow with convolutional networks
-
1
-
A. Dosovitskiy, P. Fischery, E. Ilg, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, T. Brox, et al. Flownet: Learning optical flow with convolutional networks. In ICCV, 2015. 1
-
(2015)
ICCV
-
-
Dosovitskiy, A.1
Fischery, P.2
Ilg, E.3
Hazirbas, C.4
Golkov, V.5
Van Der Smagt, P.6
Cremers, D.7
Brox, T.8
-
8
-
-
84959184995
-
Learning to generate chairs with convolutional neural networks
-
3
-
A. Dosovitskiy, J. T. Springenberg, and T. Brox. Learning to generate chairs with convolutional neural networks. In CVPR, 2015. 3
-
(2015)
CVPR
-
-
Dosovitskiy, A.1
Springenberg, J.T.2
Brox, T.3
-
10
-
-
85018936904
-
Unsupervised learning for physical interaction through video prediction
-
8
-
C. Finn, I. Goodfellow, and S. Levine. Unsupervised learning for physical interaction through video prediction. In NIPS, 2016. 8
-
(2016)
NIPS
-
-
Finn, C.1
Goodfellow, I.2
Levine, S.3
-
11
-
-
84986252211
-
Deepstereo: Learning to predict new views from the world's imagery
-
2
-
J. Flynn, I. Neulander, J. Philbin, and N. Snavely. Deepstereo: Learning to predict new views from the world's imagery. In CVPR, 2016. 2
-
(2016)
CVPR
-
-
Flynn, J.1
Neulander, I.2
Philbin, J.3
Snavely, N.4
-
13
-
-
85028031069
-
Unsupervised cnn for single view depth estimation: Geometry to the rescue
-
1, 2
-
R. Garg, V. K. BG, G. Carneiro, and I. Reid. Unsupervised cnn for single view depth estimation: Geometry to the rescue. In ECCV, 2016. 1, 2
-
(2016)
ECCV
-
-
Garg, R.1
Bg, V.K.2
Carneiro, G.3
Reid, I.4
-
14
-
-
84937849144
-
Generative adversarial nets
-
2, 3, 5
-
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D.Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, 2014. 2, 3, 5
-
(2014)
NIPS
-
-
Goodfellow, I.1
Pouget-Abadie, J.2
Mirza, M.3
Xu, B.4
Warde-Farley, D.5
Ozair, S.6
Courville, A.7
Bengio, Y.8
-
15
-
-
85019248240
-
A powerful generative model using random weights for the deep image representation
-
5
-
K. He, Y. Wang, and J. Hopcroft. A powerful generative model using random weights for the deep image representation. In NIPS, 2016. 5
-
(2016)
NIPS
-
-
He, K.1
Wang, Y.2
Hopcroft, J.3
-
18
-
-
84965096967
-
Spatial transformer networks
-
3, 4
-
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu. Spatial transformer networks. In NIPS, 2015. 3, 4
-
(2015)
NIPS
-
-
Jaderberg, M.1
Simonyan, K.2
Zisserman, A.3
Kavukcuoglu, K.4
-
20
-
-
85019245160
-
Perceptual losses for real-time style transfer and super-resolution
-
1, 2, 3, 5
-
J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 1, 2, 3, 5
-
(2016)
ECCV
-
-
Johnson, J.1
Alahi, A.2
Fei-Fei, L.3
-
22
-
-
84905756739
-
3d object manipulation in a single photograph using stock 3d models
-
1, 3
-
N. Kholgade, T. Simon, A. Efros, and Y. Sheikh. 3d object manipulation in a single photograph using stock 3d models. ACM Transactions on Computer Graphics (TOG), 2014. 1, 3
-
(2014)
ACM Transactions On Computer Graphics (TOG)
-
-
Kholgade, N.1
Simon, T.2
Efros, A.3
Sheikh, Y.4
-
26
-
-
84999041243
-
Autoencoding beyond pixels using a learned similarity metric
-
2, 3, 5
-
A. B. L. Larsen, S. K. Snderby, H. Larochelle, and Ole Winther. Autoencoding beyond pixels using a learned similarity metric. In ICML, 2016. 2, 3, 5
-
(2016)
ICML
-
-
Larsen, A.B.L.1
Snderby, S.K.2
Larochelle, H.3
Winther, O.4
-
27
-
-
85016979026
-
Stochastic multiple choice learning for training diverse deep ensembles
-
8
-
S. Lee, S. Purushwalkam, M. Cogswell, V. Ranjan, D. Crandall, and D. Batra. Stochastic multiple choice learning for training diverse deep ensembles. In NIPS, 2016. 8
-
(2016)
NIPS
-
-
Lee, S.1
Purushwalkam, S.2
Cogswell, M.3
Ranjan, V.4
Crandall, D.5
Batra, D.6
-
28
-
-
85083952137
-
Deep multi-scale video prediction beyond mean square error
-
6, 8
-
M. Mathieu, C. Couprie, and Y. LeCun. Deep multi-scale video prediction beyond mean square error. In ICLR, 2016. 6, 8
-
(2016)
ICLR
-
-
Mathieu, M.1
Couprie, C.2
LeCun, Y.3
-
30
-
-
84990062418
-
Stacked hourglass networks for human pose estimation
-
5
-
A. Newell, K. Yang, and J. Deng. Stacked hourglass networks for human pose estimation. In ECCV, 2016. 5
-
(2016)
ECCV
-
-
Newell, A.1
Yang, K.2
Deng, J.3
-
32
-
-
84990036610
-
Context encoders: Feature learning by inpainting deepak
-
3
-
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. A. Efros. Context encoders: Feature learning by inpainting deepak. In CVPR, 2016. 3
-
(2016)
CVPR
-
-
Pathak, D.1
Krähenbühl, P.2
Donahue, J.3
Darrell, T.4
Efros, A.A.5
-
33
-
-
85083950271
-
Unsupervised representation learning with deep convolutional generative adversarial networks
-
2, 3, 5
-
A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In ICLR, 2016. 2, 3, 5
-
(2016)
ICLR
-
-
Radford, A.1
Metz, L.2
Chintala, S.3
-
34
-
-
85019599710
-
-
arXiv:1602.00328, 1, 3
-
K. Rematas, C. Nguyen, T. Ritschel, M. Fritz, and T. Tuytelaars. Novel views of objects from a single image. arXiv:1602.00328, 2016. 1, 3
-
(2016)
Novel Views of Objects from a Single Image
-
-
Rematas, K.1
Nguyen, C.2
Ritschel, T.3
Fritz, M.4
Tuytelaars, T.5
-
35
-
-
85018875486
-
Improved techniques for training gans
-
2, 5
-
T. Salimans, I. Goodfellow,W. Zaremba, V. Cheung, A. Radford, and X. chen. Improved techniques for training gans. In NIPS, 2016. 2, 5
-
(2016)
NIPS
-
-
Salimans, T.1
Goodfellow, I.2
Zaremba, W.3
Cheung, V.4
Radford, A.5
Chen, X.6
-
36
-
-
84973860892
-
Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
-
7
-
H. Su, C. R. Qi, Y. Li, and L. J. Guibas. Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views. In ICCV, 2015. 7
-
(2015)
ICCV
-
-
Su, H.1
Qi, C.R.2
Li, Y.3
Guibas, L.J.4
-
37
-
-
85041920327
-
Multi-view 3d models from single images with a convolutional network
-
1, 2, 3, 6, 7
-
M. Tatarchenko, A. Dosovitskiy, and T. Brox. Multi-view 3d models from single images with a convolutional network. In ECCV, 2016. 1, 2, 3, 6, 7
-
(2016)
ECCV
-
-
Tatarchenko, M.1
Dosovitskiy, A.2
Brox, T.3
-
38
-
-
84998882079
-
Texture networks: Feed-forward synthesis of textures and stylized images
-
2, 3, 5
-
D. Ulyanov, V. Lebedev, A. Vedaldi, and V. Lempitsky. Texture networks: Feed-forward synthesis of textures and stylized images. In ICML, 2016. 2, 3, 5
-
(2016)
ICML
-
-
Ulyanov, D.1
Lebedev, V.2
Vedaldi, A.3
Lempitsky, V.4
-
41
-
-
85041907405
-
-
arXiv:1609.08546, 1
-
J. Varley, C. DeChant, A. Richardson, A. Nair, J. Ruales, and P. Allen. Shape completion enabled robotic grasping. arXiv:1609.08546, 2016. 1
-
(2016)
Shape Completion Enabled Robotic Grasping
-
-
Varley, J.1
DeChant, C.2
Richardson, A.3
Nair, A.4
Ruales, J.5
Allen, P.6
-
43
-
-
85018884809
-
An uncertain future: Forecasting from static images using variational autoencoders
-
8
-
J. Walker, C. Doersch, A. Gupta, and M. Hebert. An uncertain future: Forecasting from static images using variational autoencoders. In ECCV, 2016. 8
-
(2016)
ECCV
-
-
Walker, J.1
Doersch, C.2
Gupta, A.3
Hebert, M.4
-
44
-
-
1942436689
-
Image quality assessment: From error visibility to structural similarity
-
6
-
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 6
-
(2004)
IEEE Transactions On Image Processing
, vol.13
, Issue.4
, pp. 600-612
-
-
Wang, Z.1
Bovik, A.C.2
Sheikh, H.R.3
Simoncelli, E.P.4
-
46
-
-
85016159876
-
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
-
7
-
J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In NIPS, 2016. 7
-
(2016)
NIPS
-
-
Wu, J.1
Zhang, C.2
Xue, T.3
Freeman, W.T.4
Tenenbaum, J.B.5
-
47
-
-
85018923844
-
Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
-
8
-
T. Xue, J. Wu, K. L. Bouman, and W. T. Freeman. Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks. In NIPS, 2016. 8
-
(2016)
NIPS
-
-
Xue, T.1
Wu, J.2
Bouman, K.L.3
Freeman, W.T.4
-
48
-
-
85018933379
-
Attribute2image: Conditional image generation from visual attributes
-
8
-
X. Yan, J. Y. K. Sohn, and H. Lee. Attribute2image: Conditional image generation from visual attributes. In ECCV, 2016. 8
-
(2016)
ECCV
-
-
Yan, X.1
Sohn, J.Y.K.2
Lee, H.3
-
49
-
-
84965161391
-
Weaklysupervised disentangling with recurrent transformations for 3d view synthesis
-
1, 3
-
J. Yang, S. Reed, M.-H. Yang, and H. Lee. Weaklysupervised disentangling with recurrent transformations for 3d view synthesis. In NIPS, 2015. 1, 3
-
(2015)
NIPS
-
-
Yang, J.1
Reed, S.2
Yang, M.-H.3
Lee, H.4
-
50
-
-
85018892647
-
Learning semantic deformation flows with 3d convolutional networks
-
3
-
M. E. Yumer and N. J. Mitra. Learning semantic deformation flows with 3d convolutional networks. In ECCV, 2016. 3
-
(2016)
ECCV
-
-
Yumer, M.E.1
Mitra, N.J.2
-
51
-
-
85018883046
-
View synthesis by appearance flow
-
2, 3, 4, 6
-
T. Zhou, S. Tulsiani,W. Sun, J. Malik, and A. A. Efros. View synthesis by appearance flow. In ECCV, 2016. 2, 3, 4, 6
-
(2016)
ECCV
-
-
Zhou, T.1
Tulsiani, S.2
Sun, W.3
Malik, J.4
Efros, A.A.5
|