A causal convolutional neural network for multi-subject motion modeling and generation
Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of s...
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| Published in | Computational visual media (Beijing) Vol. 10; no. 1; pp. 45 - 59 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Beijing
Tsinghua University Press
01.02.2024
Springer Nature B.V SpringerOpen |
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| Online Access | Get full text |
| ISSN | 2096-0433 2096-0662 |
| DOI | 10.1007/s41095-022-0307-3 |
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| Abstract | Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks. |
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| AbstractList | Abstract Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks. Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks. |
| Author | Wang, Congyi Xu, Weiwei Hou, Shuaiying Bao, Hujun Chen, Yu Chai, Jinxiang Wang, Yangang Zhuang, Wenlin |
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| Cites_doi | 10.1145/2766999 10.1145/3072959.3073663 10.1109/CVPR42600.2020.00655 10.1109/ICCV48922.2021.00143 10.1155/2014/104535 10.1109/CVPR.2017.497 10.1111/cgf.14116 10.1145/3478513.3480570 10.1109/ICCV.2015.494 10.1145/3414685.3417836 10.1080/10867651.1998.10487493 10.1145/3272127.3275071 10.1145/3386569.3392422 10.1007/s11390-017-1742-y 10.1007/s11263-019-01245-6 10.1109/TPAMI.2013.248 10.1145/2897824.2925975 10.1109/CVPR46437.2021.01340 10.1109/CVPR42600.2020.00119 10.1145/1553374.1553505 10.1109/3DV.2017.00059 10.1109/TVCG.2019.2938520 10.1145/3386569.3392450 10.1609/aaai.v35i3.16321 10.1145/1730804.1730811 10.1609/aaai.v35i2.16197 10.1145/344779.344865 10.1109/ICCV48922.2021.01080 10.1109/CVPR42600.2020.00029 10.1109/CVPR42600.2020.00702 10.1145/3386569.3392462 10.1609/aaai.v34i07.6911 10.1609/aaai.v33i01.33018553 10.1145/3476576.3476651 10.1109/CVPRW.2018.00191 |
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| References | Cui, Q. J.; Sun, H. J.; Yang, F. Learning dynamic relationships for 3D human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6518–6526, 2020. GuiL YWangY XLiangX DMouraJ M FFerrariVHebertMSminchisescuCWeissYAdversarial geometry-aware human motion predictionComputer Vision–ECCV 2018. Lecture Notes in Computer Science, Vol. 11208.2018ChamSpringer823842 WangXChenQ DWangW L3D human motion editing and synthesis: A surveyComputational and Mathematical Methods in Medicine2014201410453510.1155/2014/104535 Corona, E.; Pumarola, A.; Alenyà, G.; Moreno-Noguer, F. Context-aware human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6990–6999, 2020. GhorbaniSWlokaCEtemadABrubakerM ATrojeN FProbabilistic character motion synthesis using a hierarchical deep latent variable modelComputer Graphics Forum202039822523910.1111/cgf.14116 WangZ YYuPZhaoYZhangR YZhouY FYuanJ SChenC YLearning diverse stochastic human-action generators by learning smooth latent transitionsProceedings of the AAAI Conference on Artificial Intelligence2020347122811228810.1609/aaai.v34i07.6911 Aberman, K.; Li, P. Z.; Lischinski, D.; Sorkine-Hornung, O.; Cohen-Or, D.; Chen, B. Q. Skeletonaware networks for deep motion retargeting. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 62, 2020. Xia, S. H.; Wang, C. Y.; Chai, J. X.; Hodgins, J. Realtime style transfer for unlabeled heterogeneous human motion. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 119, 2015. Wen, Y. H.; Yang, Z. P.; Fu, H. B.; Gao, L.; Sun, Y. N.; Liu, Y. J. Autoregressive stylized motion synthesis with generative flow. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13607–13607, 2021. Yuan, Y.; Kitani, K. M. Diverse trajectory forecasting with determinantal point processes. In: Proceedings of the 8th International Conference on Learning Representations, 2020. Barsoum, E.; Kender, J.; Liu, Z. C. HP-GAN: Probabilistic 3D human motion prediction via GAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 1499–149909, 2018. Starke, S.; Zhao, Y. W.; Komura, T.; Zaman, K. Local motion phases for learning multi-contact character movements. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 54, 2020. Van den Oord, A.; Dieleman, S.; Zen, H.; Simonyan, K.; Vinyals, O.; Graves, A.; Kalchbrenner, N.; Senior, A.; Kavukcuoglu, K. WaveNet: A generative model for raw audio. In: Proceedings of the 9th ISCA Speech Synthesis Workshop, 125, 2016. KunduJ NGorMBabuR VBiHMP-GAN: Bidirectional 3D human motion prediction GANProceedings of the AAAI Conference on Artificial Intelligence20193318553856010.1609/aaai.v33i01.33018553 Fragkiadaki, K.; Levine, S.; Felsen, P.; Malik, J. Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, 4346–4354, 2015. Li, M. S.; Chen, S. H.; Zhao, Y. H.; Zhang, Y.; Wang, Y. F.; Tian, Q. Dynamic multiscale graph neural networks for 3D skeleton based human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 211–220, 2020. Lee, K.; Lee, S.; Lee, J. Interactive character animation by learning multi-objective control. ACM Transactions on Graphics Vol. 37, No. 6, Article No. 180, 2018. Brand, M.; Hertzmann, A. Style machines. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, 183–192, 2000. Ghosh, A.; Cheema, N.; Oguz, C.; Theobalt, C.; Slusallek, P. Synthesis of compositional animations from textual descriptions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 1376–1386, 2021. Graves, A. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013. MaoWLiuM MSalzmannMVedaldiABischofHBroxTFrahmJ MHistory repeats itself: Human motion prediction via motion attentionComputer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12359.2020ChamSpringer474489 Taylor, G. W.; Hinton, G. E. Factored conditional restricted Boltzmann Machines for modeling motion style. In: Proceedings of the 26th Annual International Conference on Machine Learning, 1025–1032, 2009. Min, J. Y.; Liu, H. J.; Chai, J. X. Synthesis and editing of personalized stylistic human motion. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 39–46, 2010. LiuZ GLyuK DWuSChenH PHaoY BJiS LAggregated multi-GANs for controlled 3D human motion predictionProceedings of the AAAI Conference on Artificial Intelligence20213532225223210.1609/aaai.v35i3.16321 ChenZLiS CYangBLiQ HLiuHMulti-scale spatial temporal graph convolutional network for skeleton-based action recognitionProceedings of the AAAI Conference on Artificial Intelligence20213521113112210.1609/aaai.v35i2.16197 Starke, S.; Zhao, Y. W.; Zinno, F.; Komura, T. Neural animation layering for synthesizing martial arts movements. ACM Transactions on Graphics Vol. 40, No. 4, Article No. 92, 2021. BussS RIntroduction to inverse kinematics with Jacobian transpose, pseudoinverse and damped least squares methodsIEEE Journal of Robotics and Automation20041716 WangZ YChaiJ XXiaS HCombining recurrent neural networks and adversarial training for human motion synthesis and controlIEEE Transactions on Visualization and Computer Graphics2021271142810.1109/TVCG.2019.2938520 Valle-Perez, G.; Henter, G. E.; Beskow, J.; Holzapfel, A.; Oudeyer, P. Y.; Alexanderson, S. Transflower: Probabilistic autoregressive dance generation with multimodal attention. ACM Transactions on Graphics Vol. 40, No. 6, Article No. 195, 2021. NieQLiuZ WLiuY HVedaldiABischofHBroxTFrahmJ MUnsupervised 3D human pose representation with viewpoint and pose disentanglementComputer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12364.2020ChamSpringer102118 GrassiaF SPractical parameterization of rotations using the exponential mapJournal of Graphics Tools199833294810.1080/10867651.1998.10487493 PavlloDFeichtenhoferCAuliMGrangierDModeling human motion with quaternion-based neural networksInternational Journal of Computer Vision2020128485587210.1007/s11263-019-01245-6 Wang, T. C.; Liu, M. Y.; Tao, A.; Liu, G. L.; Kautz, J.; Catanzaro, B. Few-shot video-to-video synthesis. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, Article No. 451, 5013–5024, 2019. Holden, D.; Saito, J.; Komura, T. A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 138, 2016. YanX CRastogiAVillegasRSunkavalliKShechtmanEHadapSYumerELeeHFerrariVHebertMSminchisescuCWeissYMTVAE: Learning motion transformations to generate multimodal human dynamicsComputer Vision–ECCV 2018. Lecture Notes in Computer Science, Vol. 11209.2018ChamSpringer276293 Petrovich, M.; Black, M. J.; Varol, G. Action-conditioned 3D human motion synthesis with transformer VAE. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 10965–10975, 2021. Ghosh, P.; Song, J.; Aksan, E.; Hilliges, O. Learning human motion models for long-term predictions. In: Proceedings of the International Conference on 3D Vision, 458–466, 2017. Martinez, J.; Black, M. J.; Romero, J. On human motion prediction using recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4674–4683, 2017. Zhang, P. F.; Lan, C. L.; Zeng, W. J.; Xing, J. L.; Xue, J. R.; Zheng, N. N. Semantics-guided neural networks for efficient skeleton-based human action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1109–1118, 2020. Ling, H. Y.; Zinno, F.; Cheng, G.; Van De Panne, M. Character controllers using motion VAEs. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 40, 2020. Henter, G. E.; Alexanderson, S.; Beskow, J. MoGlow: Probabilistic and controllable motion synthesis using normalising flows. ACM Transactions on Graphics Vol. 39, No. 6, Article No. 236, 2020. IonescuCPapavaDOlaruVSminchisescuCHuman3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environmentsIEEE Transactions on Pattern Analysis and Machine Intelligence20143671325133910.1109/TPAMI.2013.248 Holden, D.; Komura, T.; Saito, J. Phase-functioned neural networks for character control. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 42, 2017. XuJ WXuH ZNiB BYangX KWangX LDarrellTVedaldiABischofHBroxTFrahmJ MHierarchical style-based networks for motion synthesisComputer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12356.2020ChamSpringer178194 XiaS HGaoLLaiY KYuanM ZChaiJ XA survey on human performance capture and animationJournal of Computer Science and Technology201732353655410.1007/s11390-017-1742-y 307_CR18 307_CR17 307_CR16 307_CR38 307_CR37 307_CR19 307_CR10 307_CR31 S H Xia (307_CR21) 2017; 32 X Wang (307_CR20) 2014; 2014 307_CR36 307_CR35 X C Yan (307_CR8) 2018 307_CR12 307_CR34 S Ghorbani (307_CR30) 2020; 39 307_CR33 Z Chen (307_CR2) 2021; 35 307_CR5 Z Y Wang (307_CR11) 2021; 27 307_CR4 307_CR7 Z Y Wang (307_CR14) 2020; 34 307_CR6 307_CR9 S R Buss (307_CR43) 2004; 17 307_CR29 307_CR27 307_CR26 D Pavllo (307_CR45) 2020; 128 307_CR1 307_CR42 307_CR41 307_CR40 Z G Liu (307_CR15) 2021; 35 307_CR25 307_CR24 C Ionescu (307_CR44) 2014; 36 307_CR22 Q Nie (307_CR28) 2020 F S Grassia (307_CR39) 1998; 3 L Y Gui (307_CR3) 2018 W Mao (307_CR32) 2020 J W Xu (307_CR23) 2020 J N Kundu (307_CR13) 2019; 33 |
| References_xml | – reference: Zhang, P. F.; Lan, C. L.; Zeng, W. J.; Xing, J. L.; Xue, J. R.; Zheng, N. N. Semantics-guided neural networks for efficient skeleton-based human action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1109–1118, 2020. – reference: Li, M. S.; Chen, S. H.; Zhao, Y. H.; Zhang, Y.; Wang, Y. F.; Tian, Q. Dynamic multiscale graph neural networks for 3D skeleton based human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 211–220, 2020. – reference: Henter, G. E.; Alexanderson, S.; Beskow, J. MoGlow: Probabilistic and controllable motion synthesis using normalising flows. ACM Transactions on Graphics Vol. 39, No. 6, Article No. 236, 2020. – reference: Ghosh, P.; Song, J.; Aksan, E.; Hilliges, O. Learning human motion models for long-term predictions. In: Proceedings of the International Conference on 3D Vision, 458–466, 2017. – reference: Holden, D.; Komura, T.; Saito, J. Phase-functioned neural networks for character control. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 42, 2017. – reference: WangZ YChaiJ XXiaS HCombining recurrent neural networks and adversarial training for human motion synthesis and controlIEEE Transactions on Visualization and Computer Graphics2021271142810.1109/TVCG.2019.2938520 – reference: Graves, A. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013. – reference: WangZ YYuPZhaoYZhangR YZhouY FYuanJ SChenC YLearning diverse stochastic human-action generators by learning smooth latent transitionsProceedings of the AAAI Conference on Artificial Intelligence2020347122811228810.1609/aaai.v34i07.6911 – reference: XuJ WXuH ZNiB BYangX KWangX LDarrellTVedaldiABischofHBroxTFrahmJ MHierarchical style-based networks for motion synthesisComputer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12356.2020ChamSpringer178194 – reference: Van den Oord, A.; Dieleman, S.; Zen, H.; Simonyan, K.; Vinyals, O.; Graves, A.; Kalchbrenner, N.; Senior, A.; Kavukcuoglu, K. WaveNet: A generative model for raw audio. In: Proceedings of the 9th ISCA Speech Synthesis Workshop, 125, 2016. – reference: Wen, Y. H.; Yang, Z. P.; Fu, H. B.; Gao, L.; Sun, Y. N.; Liu, Y. J. Autoregressive stylized motion synthesis with generative flow. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13607–13607, 2021. – reference: Yuan, Y.; Kitani, K. M. Diverse trajectory forecasting with determinantal point processes. In: Proceedings of the 8th International Conference on Learning Representations, 2020. – reference: Starke, S.; Zhao, Y. W.; Komura, T.; Zaman, K. Local motion phases for learning multi-contact character movements. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 54, 2020. – reference: PavlloDFeichtenhoferCAuliMGrangierDModeling human motion with quaternion-based neural networksInternational Journal of Computer Vision2020128485587210.1007/s11263-019-01245-6 – reference: Corona, E.; Pumarola, A.; Alenyà, G.; Moreno-Noguer, F. Context-aware human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6990–6999, 2020. – reference: Petrovich, M.; Black, M. J.; Varol, G. Action-conditioned 3D human motion synthesis with transformer VAE. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 10965–10975, 2021. – reference: GuiL YWangY XLiangX DMouraJ M FFerrariVHebertMSminchisescuCWeissYAdversarial geometry-aware human motion predictionComputer Vision–ECCV 2018. Lecture Notes in Computer Science, Vol. 11208.2018ChamSpringer823842 – reference: GhorbaniSWlokaCEtemadABrubakerM ATrojeN FProbabilistic character motion synthesis using a hierarchical deep latent variable modelComputer Graphics Forum202039822523910.1111/cgf.14116 – reference: Cui, Q. J.; Sun, H. J.; Yang, F. Learning dynamic relationships for 3D human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6518–6526, 2020. – reference: Barsoum, E.; Kender, J.; Liu, Z. C. HP-GAN: Probabilistic 3D human motion prediction via GAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 1499–149909, 2018. – reference: Wang, T. C.; Liu, M. Y.; Tao, A.; Liu, G. L.; Kautz, J.; Catanzaro, B. Few-shot video-to-video synthesis. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, Article No. 451, 5013–5024, 2019. – reference: Brand, M.; Hertzmann, A. Style machines. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, 183–192, 2000. – reference: Ling, H. Y.; Zinno, F.; Cheng, G.; Van De Panne, M. Character controllers using motion VAEs. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 40, 2020. – reference: YanX CRastogiAVillegasRSunkavalliKShechtmanEHadapSYumerELeeHFerrariVHebertMSminchisescuCWeissYMTVAE: Learning motion transformations to generate multimodal human dynamicsComputer Vision–ECCV 2018. Lecture Notes in Computer Science, Vol. 11209.2018ChamSpringer276293 – reference: Fragkiadaki, K.; Levine, S.; Felsen, P.; Malik, J. Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, 4346–4354, 2015. – reference: Ghosh, A.; Cheema, N.; Oguz, C.; Theobalt, C.; Slusallek, P. Synthesis of compositional animations from textual descriptions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 1376–1386, 2021. – reference: KunduJ NGorMBabuR VBiHMP-GAN: Bidirectional 3D human motion prediction GANProceedings of the AAAI Conference on Artificial Intelligence20193318553856010.1609/aaai.v33i01.33018553 – reference: Aberman, K.; Li, P. Z.; Lischinski, D.; Sorkine-Hornung, O.; Cohen-Or, D.; Chen, B. Q. Skeletonaware networks for deep motion retargeting. ACM Transactions on Graphics Vol. 39, No. 4, Article No. 62, 2020. – reference: Lee, K.; Lee, S.; Lee, J. Interactive character animation by learning multi-objective control. ACM Transactions on Graphics Vol. 37, No. 6, Article No. 180, 2018. – reference: Holden, D.; Saito, J.; Komura, T. A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 138, 2016. – reference: BussS RIntroduction to inverse kinematics with Jacobian transpose, pseudoinverse and damped least squares methodsIEEE Journal of Robotics and Automation20041716 – reference: LiuZ GLyuK DWuSChenH PHaoY BJiS LAggregated multi-GANs for controlled 3D human motion predictionProceedings of the AAAI Conference on Artificial Intelligence20213532225223210.1609/aaai.v35i3.16321 – reference: NieQLiuZ WLiuY HVedaldiABischofHBroxTFrahmJ MUnsupervised 3D human pose representation with viewpoint and pose disentanglementComputer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12364.2020ChamSpringer102118 – reference: Martinez, J.; Black, M. J.; Romero, J. On human motion prediction using recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4674–4683, 2017. – reference: WangXChenQ DWangW L3D human motion editing and synthesis: A surveyComputational and Mathematical Methods in Medicine2014201410453510.1155/2014/104535 – reference: ChenZLiS CYangBLiQ HLiuHMulti-scale spatial temporal graph convolutional network for skeleton-based action recognitionProceedings of the AAAI Conference on Artificial Intelligence20213521113112210.1609/aaai.v35i2.16197 – reference: GrassiaF SPractical parameterization of rotations using the exponential mapJournal of Graphics Tools199833294810.1080/10867651.1998.10487493 – reference: Starke, S.; Zhao, Y. W.; Zinno, F.; Komura, T. Neural animation layering for synthesizing martial arts movements. ACM Transactions on Graphics Vol. 40, No. 4, Article No. 92, 2021. – reference: Xia, S. H.; Wang, C. Y.; Chai, J. X.; Hodgins, J. Realtime style transfer for unlabeled heterogeneous human motion. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 119, 2015. – reference: IonescuCPapavaDOlaruVSminchisescuCHuman3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environmentsIEEE Transactions on Pattern Analysis and Machine Intelligence20143671325133910.1109/TPAMI.2013.248 – reference: Taylor, G. W.; Hinton, G. E. Factored conditional restricted Boltzmann Machines for modeling motion style. In: Proceedings of the 26th Annual International Conference on Machine Learning, 1025–1032, 2009. – reference: XiaS HGaoLLaiY KYuanM ZChaiJ XA survey on human performance capture and animationJournal of Computer Science and Technology201732353655410.1007/s11390-017-1742-y – reference: MaoWLiuM MSalzmannMVedaldiABischofHBroxTFrahmJ MHistory repeats itself: Human motion prediction via motion attentionComputer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12359.2020ChamSpringer474489 – reference: Valle-Perez, G.; Henter, G. E.; Beskow, J.; Holzapfel, A.; Oudeyer, P. Y.; Alexanderson, S. Transflower: Probabilistic autoregressive dance generation with multimodal attention. ACM Transactions on Graphics Vol. 40, No. 6, Article No. 195, 2021. – reference: Min, J. Y.; Liu, H. J.; Chai, J. X. Synthesis and editing of personalized stylistic human motion. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 39–46, 2010. – ident: 307_CR24 doi: 10.1145/2766999 – ident: 307_CR16 doi: 10.1145/3072959.3073663 – volume: 17 start-page: 16 year: 2004 ident: 307_CR43 publication-title: IEEE Journal of Robotics and Automation – ident: 307_CR34 doi: 10.1109/CVPR42600.2020.00655 – ident: 307_CR10 doi: 10.1109/ICCV48922.2021.00143 – ident: 307_CR4 – start-page: 823 volume-title: Computer Vision–ECCV 2018. Lecture Notes in Computer Science, Vol. 11208. year: 2018 ident: 307_CR3 – volume: 2014 start-page: 104535 year: 2014 ident: 307_CR20 publication-title: Computational and Mathematical Methods in Medicine doi: 10.1155/2014/104535 – ident: 307_CR7 doi: 10.1109/CVPR.2017.497 – ident: 307_CR35 – volume: 39 start-page: 225 issue: 8 year: 2020 ident: 307_CR30 publication-title: Computer Graphics Forum doi: 10.1111/cgf.14116 – ident: 307_CR38 doi: 10.1145/3478513.3480570 – ident: 307_CR6 doi: 10.1109/ICCV.2015.494 – ident: 307_CR37 doi: 10.1145/3414685.3417836 – volume: 3 start-page: 29 issue: 3 year: 1998 ident: 307_CR39 publication-title: Journal of Graphics Tools doi: 10.1080/10867651.1998.10487493 – ident: 307_CR40 doi: 10.1145/3272127.3275071 – ident: 307_CR9 doi: 10.1145/3386569.3392422 – volume: 32 start-page: 536 issue: 3 year: 2017 ident: 307_CR21 publication-title: Journal of Computer Science and Technology doi: 10.1007/s11390-017-1742-y – volume: 128 start-page: 855 issue: 4 year: 2020 ident: 307_CR45 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-019-01245-6 – ident: 307_CR42 – volume: 36 start-page: 1325 issue: 7 year: 2014 ident: 307_CR44 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2013.248 – ident: 307_CR31 doi: 10.1145/2897824.2925975 – ident: 307_CR25 doi: 10.1109/CVPR46437.2021.01340 – ident: 307_CR1 doi: 10.1109/CVPR42600.2020.00119 – start-page: 276 volume-title: Computer Vision–ECCV 2018. Lecture Notes in Computer Science, Vol. 11209. year: 2018 ident: 307_CR8 – ident: 307_CR5 doi: 10.1145/1553374.1553505 – ident: 307_CR41 doi: 10.1109/3DV.2017.00059 – volume: 27 start-page: 14 issue: 1 year: 2021 ident: 307_CR11 publication-title: IEEE Transactions on Visualization and Computer Graphics doi: 10.1109/TVCG.2019.2938520 – ident: 307_CR17 doi: 10.1145/3386569.3392450 – volume: 35 start-page: 2225 issue: 3 year: 2021 ident: 307_CR15 publication-title: Proceedings of the AAAI Conference on Artificial Intelligence doi: 10.1609/aaai.v35i3.16321 – ident: 307_CR27 doi: 10.1145/1730804.1730811 – start-page: 102 volume-title: Computer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12364. year: 2020 ident: 307_CR28 – volume: 35 start-page: 1113 issue: 2 year: 2021 ident: 307_CR2 publication-title: Proceedings of the AAAI Conference on Artificial Intelligence doi: 10.1609/aaai.v35i2.16197 – ident: 307_CR22 doi: 10.1145/344779.344865 – ident: 307_CR36 doi: 10.1109/ICCV48922.2021.01080 – ident: 307_CR33 doi: 10.1109/CVPR42600.2020.00029 – ident: 307_CR19 – ident: 307_CR29 doi: 10.1109/CVPR42600.2020.00702 – ident: 307_CR26 doi: 10.1145/3386569.3392462 – volume: 34 start-page: 12281 issue: 7 year: 2020 ident: 307_CR14 publication-title: Proceedings of the AAAI Conference on Artificial Intelligence doi: 10.1609/aaai.v34i07.6911 – volume: 33 start-page: 8553 issue: 1 year: 2019 ident: 307_CR13 publication-title: Proceedings of the AAAI Conference on Artificial Intelligence doi: 10.1609/aaai.v33i01.33018553 – ident: 307_CR18 doi: 10.1145/3476576.3476651 – start-page: 474 volume-title: Computer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12359. year: 2020 ident: 307_CR32 – ident: 307_CR12 doi: 10.1109/CVPRW.2018.00191 – start-page: 178 volume-title: Computer Vision–ECCV 2020. Lecture Notes in Computer Science, Vol. 12356. year: 2020 ident: 307_CR23 |
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| Snippet | Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion... Abstract Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject... |
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| SubjectTerms | Artificial Intelligence Artificial neural networks Computer Graphics Computer Science Datasets Deep learning Image Processing and Computer Vision Methods Modelling motion control motion denoising motion generation Neural networks optimization Research Article Speech recognition User Interfaces and Human Computer Interaction |
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| Title | A causal convolutional neural network for multi-subject motion modeling and generation |
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