Fetching the paper…
Reading the bibliography…
Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving.
V. Pavlovic, J. M. Rehg, and J. MacCormick, “Learning switching linear models of human motion,” in
2000
Earlier work this paper cites.
G. W. Taylor, G. E. Hinton, and S. T. Roweis, “Modeling human motion using binary latent variables,” in
2006
Earlier work this paper cites.
J. M. Wang, D. J. Fleet, and A. Hertzmann, “Gaussian process dynamical models for human motion,”
2008
Earlier work this paper cites.
I. Sutskever, G. E. Hinton, and G. W. Taylor, “The recurrent temporal restricted boltzmann machine,” in
2008
Earlier work this paper cites.
Y. Yang and D. Ramanan, “Articulated pose estimation with flexible mixtures-of-parts,” in
2011
Earlier work this paper cites.
M. Sun, P. Kohli, and J. Shotton, “Conditional regression forests for human pose estimation,” in
2012
Earlier work this paper cites.
M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele, “A database for fine grained activity detection of cooking activities,” in
2012
Earlier work this paper cites.
M. Eichner, M. Marin-Jimenez, A. Zisserman, and V. Ferrari, “2D articulated human pose estimation and retrieval in (almost) unconstrained still images,”
2012
Earlier work this paper cites.
——, “Learning spatio-temporal structure from RGB-D videos for human activity detection and anticipation.” in
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,”
2013
Earlier work this paper cites.
H. Jhuang, J. Gall, S. Zuffi, C. Schmid, and M. J. Black, “Towards understanding action recognition,” in
2013
Earlier work this paper cites.
D.-A. Huang and K. M. Kitani, “Action-reaction: Forecasting the dynamics of human interaction,” in
2014
Earlier work this paper cites.
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, “Human 3.6M: Large scale datasets and predictive methods for 3d human sensing in natural environments,”
2014
Earlier work this paper cites.
X. Chen and A. Yuille, “Articulated pose estimation by a graphical model with image dependent pairwise relations,” in
2014
Earlier work this paper cites.
A. Toshev and C. Szegedy, “DeepPose: Human pose estimation via deep neural networks,” in
2014
Earlier work this paper cites.
A. Cherian, J. Mairal, K. Alahari, and C. Schmid, “Mixing body-part sequences for human pose estimation,” in
2014
Cited alongside, same era.
S. L. Pintea, J. C. van Gemert, and A. W. Smeulders, “Déja vu,” in
2014
Cited alongside, same era.
2014
Cited alongside, same era.
J. Bayer and C. Osendorfer, “Learning stochastic recurrent networks,”
2014
Cited alongside, same era.
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, “Efficient object localization using convolutional networks,” in
2015
Cited alongside, same era.
U. Iqbal and J. Gall, “Multi-person pose estimation with local joint-to-person associations,” in
2016
Later among the works it cites.
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural-RNN: Deep learning on spatio-temporal graphs,” in
2016
Later among the works it cites.
H. S. Koppula and A. Saxena, “Anticipating human activities using object affordances for reactive robotic response,”
2016
Later among the works it cites.
2016
Later among the works it cites.
A. Shahroudy, J. Liu, T.-T. Ng, and G. Wang, “NTU RGB+D: A large scale dataset for 3D human activity analysis,” in
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Pfister, J. Charles, and A. Zisserman, “Flowing convnets for human pose estimation in videos,” in
2015
Cited alongside, same era.
K. Fragkiadaki, S. Levine, P. Felsen, and J. Malik, “Recurrent network models for human dynamics,” in
2015
Cited alongside, same era.
J. Walker, A. Gupta, and M. Hebert, “Dense optical flow prediction from a static image,” in
2015
Cited alongside, same era.
M. Mathieu, C. Couprie, and Y. LeCun, “Deep multi-scale video prediction beyond mean square error,”
2015
Cited alongside, same era.
J. Chung, K. Kastner, L. Dinh, K. Goel, A. C. Courville, and Y. Bengio, “A recurrent latent variable model for sequential data,” in
2015
Cited alongside, same era.
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, “Convolutional pose machines,”
2016
Cited alongside, same era.
A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,”
2016
Cited alongside, same era.
J. Walker, C. Doersch, A. Gupta, and M. Hebert, “An uncertain future: Forecasting from static images using variational autoencoders,” in
2016
Later among the works it cites.
C. Vondrick, H. Pirsiavash, and A. Torralba, “Generating videos with scene dynamics,”
2016
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
C. Doersch, “Tutorial on variational autoencoders,”
2016
Later among the works it cites.
M. Fraccaro, S. K. Sønderby, U. Paquet, and O. Winther, “Sequential neural models with stochastic layers,” in
2016
Later among the works it cites.
L. Wang, Y. Qiao, and X. Tang, “Mofap: A multi-level representation for action recognition,”
2016
Later among the works it cites.
F. Han, B. Reily, W. Hoff, and H. Zhang, “Space-time representation of people based on 3D skeletal data: A review,”
2017
Closest in time.
J. Martinez, M. J. Black, and J. Romero, “On human motion prediction using recurrent neural networks,” in
2017
Closest in time.