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Human motion generation aims to generate natural human pose sequences and shows immense potential for real-world applications.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proc. Adv. Neural Inform. Process. Syst. , vol. 33, 2020, pp. 1877–1901
1901
Earlier work this paper cites.
A. Gupta, S. Satkin, A. A. Efros, and M. Hebert, “From 3d scene geometry to human workspace,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2011, pp. 1961–1968
1968
Earlier work this paper cites.
A. E. Elo, The Rating of Chessplayers, Past and Present . Arco Pub., 1978
1978
Earlier work this paper cites.
B. Hommel, “Toward an action-concept model of stimulus-response compatibility,” Advances in psychology , 1997
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
S. Van Mulken, E. Andre, and J. Müller, “The persona effect: how substantial is it?” in People and computers XIII: Proceedings of HCI’98 , 1998, pp. 53–66
1998
Earlier work this paper cites.
Y. Bengio, R. Ducharme, and P. Vincent, “A neural probabilistic language model,” Proc. Adv. Neural Inform. Process. Syst. , 2000
2000
Earlier work this paper cites.
E. Grossman, M. Donnelly, R. Price, D. Pickens, V. Morgan, G. Neighbor, and R. Blake, “Brain areas involved in perception of biological motion,” Journal of cognitive neuroscience , vol. 12, no. 5, pp. 711–720, 2000
2000
Earlier work this paper cites.
S.-J. Blakemore and J. Decety, “From the perception of action to the understanding of intention,” Nature reviews neuroscience , vol. 2, no. 8, pp. 561–567, 2001
2001
Earlier work this paper cites.
T. B. Moeslund and E. Granum, “A survey of computer vision-based human motion capture,” Comput. Vis. and Image Understand. , vol. 81, no. 3, pp. 231–268, 2001
2001
Earlier work this paper cites.
N. F. Troje, “Decomposing biological motion: A framework for analysis and synthesis of human gait patterns,” Journal of vision , vol. 2, no. 5, pp. 2–2, 2002
2002
Earlier work this paper cites.
O. Arikan and D. A. Forsyth, “Interactive motion generation from examples,” ACM Trans. Graph. , vol. 21, no. 3, pp. 483–490, 2002
2002
Earlier work this paper cites.
J. Lee, J. Chai, P. S. Reitsma, J. K. Hodgins, and N. S. Pollard, “Interactive control of avatars animated with human motion data,” in Proceedings of the 29th annual conference on Computer graphics and interactive techniques , 2002, pp. 491–500
2002
Earlier work this paper cites.
L. Kovar, M. Gleicher, and F. Pighin, “Motion graphs,” ACM Trans. Graph. , vol. 21, no. 3, p. 473–482, 2002
2002
Earlier work this paper cites.
S. I. Park, H. J. Shin, and S. Y. Shin, “On-line locomotion generation based on motion blending,” in Proceedings of 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation , 2002, pp. 105–111
2002
Earlier work this paper cites.
M. Müller, T. Röder, and M. Clausen, “Efficient content-based retrieval of motion capture data,” ACM Trans. Graph. , vol. 24, no. 3, p. 677–685, 2005
2005
Earlier work this paper cites.
T. B. Moeslund, A. Hilton, and V. Krüger, “A survey of advances in vision-based human motion capture and analysis,” Comput. Vis. and Image Understand. , vol. 104, no. 2-3, pp. 90–126, 2006
2006
Earlier work this paper cites.
T. Shiratori, A. Nakazawa, and K. Ikeuchi, “Dancing-to-music character animation,” in Computer Graphics Forum , vol. 25, no. 3, 2006, pp. 449–458
2006
Earlier work this paper cites.
H. P. Shum, T. Komura, M. Shiraishi, and S. Yamazaki, “Interaction patches for multi-character animation,” ACM Trans. Graph. , vol. 27, no. 5, pp. 1–8, 2008
2008
Earlier work this paper cites.
K. Onuma, C. Faloutsos, and J. K. Hodgins, “Fmdistance: A fast and effective distance function for motion capture data.” in Eurographics (Short Papers) , 2008, pp. 83–86
2008
Earlier work this paper cites.
J. M. Saragih, S. Lucey, and J. F. Cohn, “Deformable model fitting by regularized landmark mean-shift,” Int. J. Comput. Vis. , vol. 91, pp. 200–215, 2011
2011
Earlier work this paper cites.
S. Shimada and K. Oki, “Modulation of motor area activity during observation of unnatural body movements,” Brain and cognition , vol. 80, no. 1, pp. 1–6, 2012
2012
Earlier work this paper cites.
Y. Yang and D. Ramanan, “Articulated human detection with flexible mixtures of parts,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, no. 12, pp. 2878–2890, 2012
2012
Earlier work this paper cites.
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, “Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 36, no. 7, pp. 1325–1339, 2013
2013
Earlier work this paper cites.
M. Nießner, M. Zollhöfer, S. Izadi, and M. Stamminger, “Real-time 3d reconstruction at scale using voxel hashing,” ACM Trans. Graph. , vol. 32, no. 6, pp. 1–11, 2013
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proc. Int. Conf. Learn. Represent. , 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Proc. Adv. Neural Inform. Process. Syst. , 2014
2014
Earlier work this paper cites.
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, “Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,” IEEE Trans. Pattern Anal. Mach. Intell. , 2014
2014
Earlier work this paper cites.
M. Loper, N. Mahmood, and M. J. Black, “MoSh: Motion and Shape Capture from Sparse Markers,” ACM Trans. Graph. , vol. 33, no. 6, 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , 2015
2015
Earlier work this paper cites.
D. Rezende and S. Mohamed, “Variational inference with normalizing flows,” in Proc. Int. Conf. Mach. Learn. , 2015
2015
Earlier work this paper cites.
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “Smpl: A skinned multi-person linear model,” ACM Trans. Graph. , vol. 34, no. 6, pp. 1–16, 2015
2015
Earlier work this paper cites.
N. S. Sutil, Motion and representation: The language of human movement . MIT Press, 2015
2015
Earlier work this paper cites.
J. Hodgins, “Cmu graphics lab motion capture database,” 2015
2015
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Proc. Adv. Neural Inform. Process. Syst. , vol. 28, 2015
2015
Earlier work this paper cites.
C. Mandery, Ö. Terlemez, M. Do, N. Vahrenkamp, and T. Asfour, “The kit whole-body human motion database,” in Int. Conf. on Adv. Robot. , 2015, pp. 329–336
2015
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” in Proc. Int. Conf. Learn. Represent. , 2016
2016
Earlier work this paper cites.
C. K. Sø nderby, T. Raiko, L. Maalø e, S. r. K. Sø nderby, and O. Winther, “Ladder variational autoencoders,” in Proc. Adv. Neural Inform. Process. Syst. , vol. 29, 2016, pp. 3738–3746
2016
Earlier work this paper cites.
A. Shahroudy, J. Liu, T.-T. Ng, and G. Wang, “Ntu rgb+d: A large scale dataset for 3d human activity analysis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 1010–1019
2016
Earlier work this paper cites.
M. Plappert, C. Mandery, and T. Asfour, “The kit motion-language dataset,” Big Data , vol. 4, no. 4, pp. 236–252, 2016
2016
Earlier work this paper cites.
J. Xu, T. Mei, T. Yao, and Y. Rui, “Msr-vtt: A large video description dataset for bridging video and language,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 5288–5296
2016
Earlier work this paper cites.
S. Agrawal and M. van de Panne, “Task-based locomotion,” ACM Trans. Graph. , vol. 35, no. 4, pp. 1–11, 2016
2016
Earlier work this paper cites.
M. Savva, A. X. Chang, P. Hanrahan, M. Fisher, and M. Nießner, “Pigraphs: learning interaction snapshots from observations,” ACM Trans. Graph. , vol. 35, no. 4, pp. 1–12, 2016
2016
Earlier work this paper cites.
P. Garrido, M. Zollhöfer, D. Casas, L. Valgaerts, K. Varanasi, P. Pérez, and C. Theobalt, “Reconstruction of personalized 3d face rigs from monocular video,” ACM Trans. Graph. , vol. 35, no. 3, pp. 1–15, 2016
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Proc. Adv. Neural Inform. Process. Syst. , vol. 29, 2016
2016
Earlier work this paper cites.
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” Int. Conf. on 3D Vis. , 2017
2017
Earlier work this paper cites.
J. Romero, D. Tzionas, and M. J. Black, “Embodied hands: Modeling and capturing hands and bodies together,” ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) , vol. 36, no. 6, 2017
2017
Earlier work this paper cites.
J. Martinez, R. Hossain, J. Romero, and J. J. Little, “A simple yet effective baseline for 3d human pose estimation,” in Proc. Int. Conf. Comput. Vis. , 2017, pp. 2640–2649
2017
Earlier work this paper cites.
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 7291–7299
2017
Earlier work this paper cites.
A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” in Proc. Adv. Neural Inform. Process. Syst. , vol. 30, 2017
2017
Earlier work this paper cites.
O. Alemi, J. Françoise, and P. Pasquier, “Groovenet: Real-time music-driven dance movement generation using artificial neural networks,” networks , vol. 8, no. 17, p. 26, 2017
2017
Earlier work this paper cites.
K. Takeuchi, S. Kubota, K. Suzuki, D. Hasegawa, and H. Sakuta, “Creating a gesture-speech dataset for speech-based automatic gesture generation,” in International Conference on Human-Computer Interaction , 2017, pp. 198–202
2017
Earlier work this paper cites.
D. Holden, T. Komura, and J. Saito, “Phase-functioned neural networks for character control,” ACM Trans. Graph. , vol. 36, no. 4, pp. 1–13, 2017
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 652–660
2017
Earlier work this paper cites.
D. Tome, C. Russell, and L. Agapito, “Lifting from the deep: Convolutional 3d pose estimation from a single image,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 2500–2509
2017
Earlier work this paper cites.
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 5828–5839
2017
Earlier work this paper cites.
A. Haarbach, T. Birdal, and S. Ilic, “Survey of higher order rigid body motion interpolation methods for keyframe animation and continuous-time trajectory estimation,” in Int. Conf. on 3D Vis. , 2018, pp. 381–389
2018
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in Proc. Int. Conf. Learn. Represent. , 2018
2018
Earlier work this paper cites.
Y. Ji, F. Xu, Y. Yang, F. Shen, H. T. Shen, and W.-S. Zheng, “A large-scale rgb-d database for arbitrary-view human action recognition,” in Proc. ACM Int. Conf. Multimedia , 2018, p. 1510–1518
2018
Earlier work this paper cites.
H. Ahn, T. Ha, Y. Choi, H. Yoo, and S. Oh, “Text2action: Generative adversarial synthesis from language to action,” in Int. Conf. on Robot. and Automa. , 2018, pp. 5915–5920
2018
Earlier work this paper cites.
T. Tang, J. Jia, and H. Mao, “Dance with melody: An lstm-autoencoder approach to music-oriented dance synthesis,” in Proc. ACM Int. Conf. Multimedia , 2018, pp. 1598–1606
2018
Earlier work this paper cites.
Y. Ferstl and R. McDonnell, “Investigating the use of recurrent motion modelling for speech gesture generation,” in Proceedings of the 18th International Conference on Intelligent Virtual Agents , 2018, p. 93–98
2018
Earlier work this paper cites.
C. T. Ishi, D. Machiyashiki, R. Mikata, and H. Ishiguro, “A speech-driven hand gesture generation method and evaluation in android robots,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3757–3764, 2018
2018
Earlier work this paper cites.
T. Kucherenko, D. Hasegawa, G. E. Henter, N. Kaneko, and H. Kjellström, “Analyzing input and output representations for speech-driven gesture generation,” in Proc. Int. Conf. on Intelligent Virtual Agents , 2019, p. 97–104
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019
2019
Earlier work this paper cites.
G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. A. Osman, D. Tzionas, and M. J. Black, “Expressive body capture: 3D hands, face, and body from a single image,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 10 975–10 985
2019
Earlier work this paper cites.
D. Pavllo, C. Feichtenhofer, D. Grangier, and M. Auli, “3d human pose estimation in video with temporal convolutions and semi-supervised training,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 7753–7762
2019
Earlier work this paper cites.
N. Mahmood, N. Ghorbani, N. F. Troje, G. Pons-Moll, and M. J. Black, “AMASS: Archive of motion capture as surface shapes,” in Proc. Int. Conf. Comput. Vis. , Oct. 2019, pp. 5441–5450
2019
Earlier work this paper cites.
H. Joo, T. Simon, M. Cikara, and Y. Sheikh, “Towards social artificial intelligence: Nonverbal social signal prediction in a triadic interaction,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 10 873–10 883
2019
Earlier work this paper cites.
G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. Osman, D. Tzionas, and M. J. Black, “Expressive body capture: 3d hands, face, and body from a single image,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 10 975–10 985
2019
Earlier work this paper cites.
K. Iskakov, E. Burkov, V. Lempitsky, and Y. Malkov, “Learnable triangulation of human pose,” in Proc. Int. Conf. Comput. Vis. , 2019, pp. 7718–7727
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2019, pp. 4401–4410
2019
Earlier work this paper cites.
C. Ahuja and L. Morency, “Language2pose: Natural language grounded pose forecasting,” in Int. Conf. on 3D Vis. , 2019, pp. 719–728
2019
Earlier work this paper cites.
H.-Y. Lee, X. Yang, M.-Y. Liu, T.-C. Wang, Y.-D. Lu, M.-H. Yang, and J. Kautz, “Dancing to music,” in Proc. Adv. Neural Inform. Process. Syst. , vol. 32, 2019
2019
Earlier work this paper cites.
S. Ginosar, A. Bar, G. Kohavi, C. Chan, A. Owens, and J. Malik, “Learning individual styles of conversational gesture,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2019
2019
Earlier work this paper cites.
Y. Yoon, W.-R. Ko, M. Jang, J. Lee, J. Kim, and G. Lee, “Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots,” in Int. Conf. on Robot. and Automa. , 2019, pp. 4303–4309
2019
Cited alongside, same era.
M. Hassan, V. Choutas, D. Tzionas, and M. J. Black, “Resolving 3d human pose ambiguities with 3d scene constraints,” in Proc. Int. Conf. Comput. Vis. , 2019, pp. 2282–2292
2019
Cited alongside, same era.
A. Monszpart, P. Guerrero, D. Ceylan, E. Yumer, and N. J. Mitra, “iMapper: Interaction-guided scene mapping from monocular videos,” SIGGRAPH , 2019
2019
Cited alongside, same era.
S. Starke, H. Zhang, T. Komura, and J. Saito, “Neural state machine for character-scene interactions.” ACM Trans. Graph. , vol. 38, no. 6, pp. 209–1, 2019
2019
Cited alongside, same era.
J. Kim, H. Oh, S. Kim, H. Tong, and S. Lee, “A brand new dance partner: Music-conditioned pluralistic dancing controlled by multiple dance genres,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 3490–3500
2022
Later among the works it cites.
J. Gao, J. Pu, H. Zhang, Y. Shan, and W.-S. Zheng, “Pc-dance: Posture-controllable music-driven dance synthesis,” in Proc. ACM Int. Conf. Multimedia , 2022, p. 1261–1269
2022
Later among the works it cites.
T. Yin, L. Hoyet, M. Christie, M.-P. Cani, and J. Pettré, “The one-man-crowd: Single user generation of crowd motions using virtual reality,” IEEE Trans. Vis. Comput. Graph. , vol. 28, no. 5, pp. 2245–2255, 2022
2022
Later among the works it cites.
Z. Wang, Y. Chen, T. Liu, Y. Zhu, W. Liang, and S. Huang, “HUMANISE: Language-conditioned human motion generation in 3d scenes,” in Proc. Adv. Neural Inform. Process. Syst. , 2022
2022
Later among the works it cites.
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S. Tsuchida, S. Fukayama, M. Hamasaki, and M. Goto, “Aist dance video database: Multi-genre, multi-dancer, and multi-camera database for dance information processing.” in Int. Soc. for Music Infor. Retri. Conf. , vol. 1, no. 5, 2019, p. 6
2019
Cited alongside, same era.
N. Mahmood, N. Ghorbani, N. F. Troje, G. Pons-Moll, and M. J. Black, “Amass: Archive of motion capture as surface shapes,” in Proc. Int. Conf. Comput. Vis. , 2019, pp. 5442–5451
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. Gopalakrishnan, A. Mali, D. Kifer, L. Giles, and A. G. Ororbia, “A neural temporal model for human motion prediction,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 12 116–12 125
2019
Cited alongside, same era.
A. Hernandez, J. Gall, and F. Moreno-Noguer, “Human motion prediction via spatio-temporal inpainting,” in Proc. Int. Conf. Comput. Vis. , 2019, pp. 7134–7143
2019
Cited alongside, same era.
N. Sadoughi and C. Busso, “Speech-driven animation with meaningful behaviors,” Speech Communication , vol. 110, pp. 90–100, 2019
2019
Cited alongside, same era.
B. Li, X. Qi, T. Lukasiewicz, and P. Torr, “Controllable text-to-image generation,” Proc. Adv. Neural Inform. Process. Syst. , vol. 32, 2019
2019
Cited alongside, same era.
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik et al. , “Habitat: A platform for embodied ai research,” in Proc. Int. Conf. Comput. Vis. , 2019, pp. 9339–9347
2019
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Proc. Adv. Neural Inform. Process. Syst. , vol. 35, pp. 27 730–27 744, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
I. Skorokhodov, S. Tulyakov, and M. Elhoseiny, “Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 3626–3636
2022
Later among the works it cites.
S. Yu, J. Tack, S. Mo, H. Kim, J. Kim, J.-W. Ha, and J. Shin, “Generating videos with dynamics-aware implicit generative adversarial networks,” in Proc. Int. Conf. Learn. Represent. , 2022
2022
Later among the works it cites.
J. Gao, T. Shen, Z. Wang, W. Chen, K. Yin, D. Li, O. Litany, Z. Gojcic, and S. Fidler, “Get3d: A generative model of high quality 3d textured shapes learned from images,” in Proc. Adv. Neural Inform. Process. Syst. , 2022
2022
Later among the works it cites.
Z. Cai, D. Ren, A. Zeng, Z. Lin, T. Yu, W. Wang, X. Fan, Y. Gao, Y. Yu, L. Pan, F. Hong, M. Zhang, C. C. Loy, L. Yang, and Z. Liu, “Humman: Multi-modal 4d human dataset for versatile sensing and modeling,” in Proc. Eur. Conf. Comput. Vis. , October 2022
2022
Later among the works it cites.
K. Lyu, H. Chen, Z. Liu, B. Zhang, and R. Wang, “3d human motion prediction: A survey,” Neurocomputing , vol. 489, pp. 345–365, 2022
2022
Later among the works it cites.
L. Mourot, L. Hoyet, F. Le Clerc, F. Schnitzler, and P. Hellier, “A survey on deep learning for skeleton-based human animation,” in Computer Graphics Forum , vol. 41, no. 1, 2022, pp. 122–157
2022
Later among the works it cites.
W. Liu, Q. Bao, Y. Sun, and T. Mei, “Recent advances of monocular 2d and 3d human pose estimation: a deep learning perspective,” ACM Computing Surveys , vol. 55, no. 4, pp. 1–41, 2022
2022
Later among the works it cites.
M. Suzuki and Y. Matsuo, “A survey of multimodal deep generative models,” Advanced Robotics , vol. 36, no. 5-6, pp. 261–278, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Zhang, B. L. Bhatnagar, S. Starke, V. Guzov, and G. Pons-Moll, “Couch: towards controllable human-chair interactions,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 518–535
2022
Later among the works it cites.
H. Ye, W. Zhu, C. Wang, R. Wu, and Y. Wang, “Faster voxelpose: Real-time 3d human pose estimation by orthographic projection,” in Proc. Eur. Conf. Comput. Vis. , 2022
2022
Later among the works it cites.
B. Degardin, J. Neves, V. Lopes, J. Brito, E. Yaghoubi, and H. Proença, “Generative adversarial graph convolutional networks for human action synthesis,” in Proc. IEEE Winter Conf. on Appl. of Comput. Vis. , 2022, pp. 1150–1159
2022
Later among the works it cites.
Q. Lu, Y. Zhang, M. Lu, and V. Roychowdhury, “Action-conditioned on-demand motion generation,” in Proc. ACM Int. Conf. Multimedia , 2022, pp. 2249–2257
2022
Later among the works it cites.
T. Lucas*, F. Baradel*, P. Weinzaepfel, and G. Rogez, “Posegpt: Quantization-based 3d human motion generation and forecasting,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 417–435
2022
Later among the works it cites.
P. Cervantes, Y. Sekikawa, I. Sato, and K. Shinoda, “Implicit neural representations for variable length human motion generation,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 356–372
2022
Later among the works it cites.
F. Hong, M. Zhang, L. Pan, Z. Cai, L. Yang, and Z. Liu, “Avatarclip: Zero-shot text-driven generation and animation of 3d avatars,” ACM Trans. Graph. , vol. 41, no. 4, pp. 1–19, 2022
2022
Later among the works it cites.
G. Tevet, B. Gordon, A. Hertz, A. H. Bermano, and D. Cohen-Or, “Motionclip: Exposing human motion generation to clip space,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 358–374
2022
Later among the works it cites.
M. Petrovich, M. J. Black, and G. Varol, “TEMOS: Generating diverse human motions from textual descriptions,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 480–497
2022
Later among the works it cites.
C. Guo, X. Zuo, S. Wang, and L. Cheng, “Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 580–597
2022
Later among the works it cites.
N. Athanasiou, M. Petrovich, M. J. Black, and G. Varol, “Teach: Temporal action compositions for 3d humans,” in Int. Conf. on 3D Vis. , September 2022, pp. 414–423
2022
Later among the works it cites.
B. Li, Y. Zhao, S. Zhelun, and L. Sheng, “Danceformer: Music conditioned 3d dance generation with parametric motion transformer,” in Proc. Assoc. Advance. Artif. Intell. , 2022, pp. 1272–1279
2022
Later among the works it cites.
L. Siyao, W. Yu, T. Gu, C. Lin, Q. Wang, C. Qian, C. C. Loy, and Z. Liu, “Bailando: 3d dance generation via actor-critic gpt with choreographic memory,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2022, pp. 11 050–11 059
2022
Later among the works it cites.
H. Y. Au, J. Chen, J. Jiang, and Y. Guo, “Choreograph: Music-conditioned automatic dance choreography over a style and tempo consistent dynamic graph,” in Proc. ACM Int. Conf. Multimedia , 2022, p. 3917–3925
2022
Later among the works it cites.
Z. Wang, J. Jia, H. Wu, J. Xing, J. Cai, F. Meng, G. Chen, and Y. Wang, “Groupdancer: Music to multi-people dance synthesis with style collaboration,” in Proc. ACM Int. Conf. Multimedia , 2022, p. 1138–1146
2022
Later among the works it cites.
J. Sun, C. Wang, H. Hu, H. Lai, Z. Jin, and J.-F. Hu, “You never stop dancing: Non-freezing dance generation via bank-constrained manifold projection,” in Proc. Adv. Neural Inform. Process. Syst. , 2022
2022
Later among the works it cites.
A. Aristidou, A. Yiannakidis, K. Aberman, D. Cohen-Or, A. Shamir, and Y. Chrysanthou, “Rhythm is a dancer: Music-driven motion synthesis with global structure,” IEEE Trans. Vis. Comput. Graph. , pp. 1–1, 2022
2022
Later among the works it cites.
X. Liu, Q. Wu, H. Zhou, Y. Xu, R. Qian, X. Lin, X. Zhou, W. Wu, B. Dai, and B. Zhou, “Learning hierarchical cross-modal association for co-speech gesture generation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2022, pp. 10 462–10 472
2022
Later among the works it cites.
H. Liu, N. Iwamoto, Z. Zhu, Z. Li, Y. Zhou, E. Bozkurt, and B. Zheng, “Disco: Disentangled implicit content and rhythm learning for diverse co-speech gestures synthesis,” in Proc. ACM Int. Conf. Multimedia , 2022, p. 3764–3773
2022
Later among the works it cites.
H. Liu, Z. Zhu, N. Iwamoto, Y. Peng, Z. Li, Y. Zhou, E. Bozkurt, and B. Zheng, “Beat: A large-scale semantic and emotional multi-modal dataset for conversational gestures synthesis,” Proc. Eur. Conf. Comput. Vis. , 2022
2022
Later among the works it cites.
I. Habibie, M. Elgharib, K. Sarkar, A. Abdullah, S. Nyatsanga, M. Neff, and C. Theobalt, “A motion matching-based framework for controllable gesture synthesis from speech,” in SIGGRAPH , 2022
2022
Later among the works it cites.
T. Ao, Q. Gao, Y. Lou, B. Chen, and L. Liu, “Rhythmic gesticulator: Rhythm-aware co-speech gesture synthesis with hierarchical neural embeddings,” ACM Trans. Graph. , vol. 41, no. 6, nov 2022
2022
Later among the works it cites.
I. Mason, S. Starke, and T. Komura, “Real-time style modelling of human locomotion via feature-wise transformations and local motion phases,” Proc. ACM Comput. Graph. Interact. Tech. , vol. 5, no. 1, may 2022
2022
Later among the works it cites.
J. Wang, Y. Rong, J. Liu, S. Yan, D. Lin, and B. Dai, “Towards diverse and natural scene-aware 3d human motion synthesis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 20 460–20 469
2022
Later among the works it cites.
Y. Zhang and S. Tang, “The wanderings of odysseus in 3d scenes,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 20 481–20 491
2022
Later among the works it cites.
O. Taheri, V. Choutas, M. J. Black, and D. Tzionas, “Goal: Generating 4d whole-body motion for hand-object grasping,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 13 263–13 273
2022
Later among the works it cites.
Y. Wu, J. Wang, Y. Zhang, S. Zhang, O. Hilliges, F. Yu, and S. Tang, “Saga: Stochastic whole-body grasping with contact,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 257–274
2022
Later among the works it cites.
W. Mao, miaomiao Liu, R. Hartley, and M. Salzmann, “Contact-aware human motion forecasting,” in Proc. Adv. Neural Inform. Process. Syst. , 2022
2022
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 10 684–10 695
2022
Later among the works it cites.
J.-H. Kim, Y. Kim, J. Lee, K. M. Yoo, and S.-W. Lee, “Mutual information divergence: A unified metric for multimodal generative models,” Proc. Adv. Neural Inform. Process. Syst. , vol. 35, pp. 35 072–35 086, 2022
2022
Later among the works it cites.
S. Alexanderson, R. Nagy, J. Beskow, and G. E. Henter, “Listen, denoise, action! audio-driven motion synthesis with diffusion models,” ACM Trans. Graph. , vol. 42, no. 4, pp. 1–20, 2023
2023
Closest in time.
T. Ao, Z. Zhang, and L. Liu, “Gesturediffuclip: Gesture diffusion model with clip latents,” ACM Trans. Graph. , 2023
2023
Closest in time.
G. Tevet, S. Raab, B. Gordon, Y. Shafir, D. Cohen-or, and A. H. Bermano, “Human motion diffusion model,” in Proc. Int. Conf. Learn. Represent. , 2023
2023
Closest in time.
J. Tseng, R. Castellon, and K. Liu, “Edge: Editable dance generation from music,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023, pp. 448–458
2023
Closest in time.
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” in Proc. Int. Conf. Learn. Represent. , 2023
2023
Closest in time.
S. M. A. Akber, S. N. Kazmi, S. M. Mohsin, and A. Szczęsna, “Deep learning-based motion style transfer tools, techniques and future challenges,” Sensors , vol. 23, no. 5, p. 2597, 2023
2023
Closest in time.
X. Ma, J. Su, C. Wang, W. Zhu, and Y. Wang, “3d human mesh estimation from virtual markers,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023, pp. 534–543
2023
Closest in time.
T. Lee, G. Moon, and K. M. Lee, “Multiact: Long-term 3d human motion generation from multiple action labels,” in Proc. Assoc. Advance. Artif. Intell. , 2023, pp. 1231–1239
2023
Closest in time.
C. Xin, B. Jiang, W. Liu, Z. Huang, B. Fu, T. Chen, J. Yu, and G. Yu, “Executing your commands via motion diffusion in latent space,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2023, pp. 18 000–18 010
2023
Closest in time.
J. Kim, J. Kim, and S. Choi, “Flame: Free-form language-based motion synthesis & editing,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 7, 2023, pp. 8255–8263
2023
Closest in time.
J. Zhang, Y. Zhang, X. Cun, Y. Zhang, H. Zhao, H. Lu, X. Shen, and Y. Shan, “T2m-gpt: Generating human motion from textual descriptions with discrete representations,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2023, pp. 14 730–14 740
2023
Closest in time.
J. Lin, J. Chang, L. Liu, G. Li, L. Lin, Q. Tian, and C.-W. Chen, “Being comes from not-being: Open-vocabulary text-to-motion generation with wordless training,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2023, pp. 23 222–23 231
2023
Closest in time.
Z. Zhou and B. Wang, “Ude: A unified driving engine for human motion generation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2023, pp. 5632–5641
2023
Closest in time.
R. Dabral, M. H. Mughal, V. Golyanik, and C. Theobalt, “Mofusion: A framework for denoising-diffusion-based motion synthesis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , June 2023, pp. 9760–9770
2023
Closest in time.
N. Le, T. Pham, T. Do, E. Tjiputra, Q. D. Tran, and A. Nguyen, “Music-driven group choreography,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023
2023
Closest in time.
L. Zhu, X. Liu, X. Liu, R. Qian, Z. Liu, and L. Yu, “Taming diffusion models for audio-driven co-speech gesture generation,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023
2023
Closest in time.
H. Yi, H. Liang, Y. Liu, Q. Cao, Y. Wen, T. Bolkart, D. Tao, and M. J. Black, “Generating holistic 3d human motion from speech,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023
2023
Closest in time.
S. Yang, Z. Wu, M. Li, Z. Zhang, L. Hao, W. Bao, and H. Zhuang, “Qpgesture: Quantization-based and phase-guided motion matching for natural speech-driven gesture generation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023
2023
Closest in time.
S. Ghorbani, Y. Ferstl, D. Holden, N. F. Troje, and M.-A. Carbonneau, “Zeroeggs: Zero-shot example-based gesture generation from speech,” in Computer Graphics Forum , vol. 42, no. 1, 2023, pp. 206–216
2023
Closest in time.
A. Ghosh, R. Dabral, V. Golyanik, C. Theobalt, and P. Slusallek, “Imos: Intent-driven full-body motion synthesis for human-object interactions,” in Eurographics , 2023
2023
Closest in time.
2023
Closest in time.
J. P. Araújo, J. Li, K. Vetrivel, R. Agarwal, J. Wu, D. Gopinath, A. W. Clegg, and K. Liu, “Circle: Capture in rich contextual environments,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023, pp. 21 211–21 221
2023
Closest in time.
H. Zhang, Y. Tian, Y. Zhang, M. Li, L. An, Z. Sun, and Y. Liu, “Pymaf-x: Towards well-aligned full-body model regression from monocular images,” IEEE Trans. Pattern Anal. Mach. Intell. , 2023
2023
Closest in time.
W. Zhu, X. Ma, Z. Liu, L. Liu, W. Wu, and Y. Wang, “Learning human motion representations: A unified perspective,” in Proc. Int. Conf. Comput. Vis. , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
R. Mokady, A. Hertz, K. Aberman, Y. Pritch, and D. Cohen-Or, “Null-text inversion for editing real images using guided diffusion models,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2023, pp. 6038–6047
2023
Closest in time.
X. Pan, A. Tewari, T. Leimkühler, L. Liu, A. Meka, and C. Theobalt, “Drag your gan: Interactive point-based manipulation on the generative image manifold,” in SIGGRAPH , 2023
2023
Closest in time.
C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun et al. , “Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation,” in Conference on Robot Learning . PMLR, 2023, pp. 80–93
2023
Closest in time.
C. Guo, X. Zuo, S. Wang, S. Zou, Q. Sun, A. Deng, M. Gong, and L. Cheng, “Action2motion: Conditioned generation of 3d human motions,” in Proc. ACM Int. Conf. Multimedia , 2020, pp. 2021–2029
2029
Closest in time.