Fetching the paper…
Reading the bibliography…
We introduce MotionScript, a novel framework for generating highly detailed, natural language descriptions of 3D human motions.
V. Chvatal, “A greedy heuristic for the set-covering problem,” Mathematics of operations research , 1979
1979
Earlier work this paper cites.
L. Bourdev and J. Malik, “Poselets: Body part detectors trained using 3d human pose annotations,” in ICCV , 2009
2009
Earlier work this paper cites.
T. Hachaj and M. R. Ogiela, “Semantic description and recognition of human body poses and movement sequences with gesture description language,” in International Conference on Bio-Science and Bio-Technology , 2012
2012
Earlier work this paper cites.
G. Pons-Moll, D. J. Fleet, and B. Rosenhahn, “Posebits for monocular human pose estimation,” in CVPR , 2014
2014
Earlier work this paper cites.
M. Plappert, C. Mandery, and T. Asfour, “The kit motion-language dataset,” Big data , 2016
2016
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, Nov. 2017
2017
Earlier work this paper cites.
G. Pavlakos, X. Zhou, and K. Daniilidis, “Ordinal depth supervision for 3d human pose estimation,” in CVPR , 2018
2018
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 ICCV , 2019
2019
Earlier work this paper cites.
C. Ahuja and L.-P. Morency, “Language2pose: Natural language grounded pose forecasting,” in International Conference on 3D Vision (3DV) . IEEE, 2019
2019
Earlier work this paper cites.
S. Starke, H. Zhang, T. Komura, and J. Saito, “Neural state machine for character-scene interactions.” ACM Trans. Graph. , 2019
2019
Earlier work this paper cites.
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 ACM Multimedia , 2020
2020
Earlier work this paper cites.
Y. Yuan and K. Kitani, “Dlow: Diversifying latent flows for diverse human motion prediction,” in ECCV . Springer, 2020
2020
Earlier work this paper cites.
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 ACM International Conference on Multimedia , 2020
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. , “Language models are few-shot learners,” NeurIPS , 2020
2020
Earlier work this paper cites.
B. Biro, Z. Zhang, M. Chen, and A. Lim, “The sfu-store-nav 3d virtual human platform for human-aware robotics,” in ICRA, Workshop on Long-term Human Motion Prediction , 2021
2021
Earlier work this paper cites.
A. R. Punnakkal, A. Chandrasekaran, N. Athanasiou, A. Quiros-Ramirez, and M. J. Black, “Babel: Bodies, action and behavior with english labels,” in CVPR , 2021
2021
Earlier work this paper cites.
M. Petrovich, M. J. Black, and G. Varol, “Action-conditioned 3d human motion synthesis with transformer vae,” in ICCV , 2021
2021
Earlier work this paper cites.
M. Petrovich, M. J. Black, and G. Varol, “Action-conditioned 3d human motion synthesis with transformer vae,” in ICCV , 2021
2021
Earlier work this paper cites.
R. Li, S. Yang, D. A. Ross, and A. Kanazawa, “Ai choreographer: Music conditioned 3d dance generation with aist++,” in ICCV , 2021
2021
Earlier work this paper cites.
A. Ghosh, N. Cheema, C. Oguz, C. Theobalt, and P. Slusallek, “Synthesis of compositional animations from textual descriptions,” in ICCV , 2021
2021
Earlier work this paper cites.
H. Kim, A. Zala, G. Burri, and M. Bansal, “Fixmypose: Pose correctional captioning and retrieval,” in AAAI , 2021
2021
Earlier work this paper cites.
M. Fieraru, M. Zanfir, S. C. Pirlea, V. Olaru, and C. Sminchisescu, “Aifit: Automatic 3d human-interpretable feedback models for fitness training,” in CVPR , 2021
2021
Earlier work this paper cites.
S. Kulal, J. Mao, A. Aiken, and J. Wu, “Hierarchical motion understanding via motion programs,” in CVPR , 2021
2021
Cited alongside, same era.
2022
Cited alongside, same era.
C. Guo, S. Zou, X. Zuo, S. Wang, W. Ji, X. Li, and L. Cheng, “Generating diverse and natural 3d human motions from text,” in CVPR , 2022
2022
Cited alongside, same era.
M. Petrovich, M. J. Black, and G. Varol, “Temos: Generating diverse human motions from textual descriptions,” in ECCV , 2022
2022
Cited alongside, same era.
N. Athanasiou, M. Petrovich, M. J. Black, and G. Varol, “Teach: Temporal action composition for 3d humans,” in 3DV , 2022
2022
Cited alongside, same era.
Y. Yuan, J. Song, U. Iqbal, A. Vahdat, and J. Kautz, “Physdiff: Physics-guided human motion diffusion model,” in ICCV , 2023
2023
Closest in time.
R. Dabral, M. H. Mughal, V. Golyanik, and C. Theobalt, “Mofusion: A framework for denoising-diffusion-based motion synthesis,” in CVPR , 2023
2023
Closest in time.
J. Kim, J. Kim, and S. Choi, “Flame: Free-form language-based motion synthesis & editing,” in AAAI , 2023
2023
Closest in time.
Y. Jiang, S. Yang, T. L. Koh, W. Wu, C. C. Loy, and Z. Liu, “Text2performer: Text-driven human video generation,” ICCV , 2023
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. Delmas, P. Weinzaepfel, T. Lucas, F. Moreno-Noguer, and G. Rogez, “PoseScript: 3D Human Poses from Natural Language,” in ECCV , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
C. Zhong, L. Hu, Z. Zhang, Y. Ye, and S. Xia, “Spatio-temporal gating-adjacency gcn for human motion prediction,” in CVPR , 2022
2022
Cited alongside, same era.
Y. Liu, R. Cadei, J. Schweizer, S. Bahmani, and A. Alahi, “Towards robust and adaptive motion forecasting: A causal representation perspective,” in CVPR , 2022
2022
Cited alongside, same era.
P. Li, K. Aberman, Z. Zhang, R. Hanocka, and O. Sorkine-Hornung, “Ganimator: Neural motion synthesis from a single sequence,” TOG , 2022
2022
Cited alongside, same era.
P. J. Yazdian, M. Chen, and A. Lim, “Gesture2vec: Clustering gestures using representation learning methods for co-speech gesture generation,” in IROS . IEEE, 2022
2022
Cited alongside, same era.
D. Moltisanti, J. Wu, B. Dai, and C. C. Loy, “Brace: The breakdancing competition dataset for dance motion synthesis,” in ECCV , 2022
2022
Cited alongside, same era.
S. Alexanderson, R. Nagy, J. Beskow, and G. E. Henter, “Listen, denoise, action! audio-driven motion synthesis with diffusion models,” TOG , 2023
2023
Closest in time.
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,” in CVPR , 2023
2023
Closest in time.
S. Xu, Z. Li, Y.-X. Wang, and L.-Y. Gui, “Interdiff: Generating 3d human-object interactions with physics-informed diffusion,” in ICCV , 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 , 2023
2023
Closest in time.
Y. Qian, J. Urbanek, A. G. Hauptmann, and J. Won, “Breaking the limits of text-conditioned 3d motion synthesis with elaborative descriptions,” in ICCV , 2023
2023
Closest in time.
S. Yang, Z. Wang, Z. Wu, M. Li, Z. Zhang, Q. Huang, L. Hao, S. Xu, X. Wu, C. Yang, et al. , “Unifiedgesture: A unified gesture synthesis model for multiple skeletons,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
J. Tseng, R. Castellon, and K. Liu, “Edge: Editable dance generation from music,” in CVPR , 2023
2023
Closest in time.
2023
Closest in time.
Y. Wang, Z. Leng, F. W. Li, S.-C. Wu, and X. Liang, “Fg-t2m: Fine-grained text-driven human motion generation via diffusion model,” in ICCV , 2023
2023
Closest in time.
W. Xiang, C. Li, Y. Zhou, B. Wang, and L. Zhang, “Generative action description prompts for skeleton-based action recognition,” in ICCV , 2023
2023
Closest in time.
S. S. Kalakonda, S. Maheshwari, and R. K. Sarvadevabhatla, “Action-gpt: Leveraging large-scale language models for improved and generalized action generation,” in ICME . IEEE, 2023
2023
Closest in time.
J. Zhang, Y. Zhang, X. Cun, S. Huang, Y. Zhang, H. Zhao, H. Lu, and X. Shen, “T2m-gpt: Generating human motion from textual descriptions with discrete representations,” in CVPR , 2023
2023
Closest in time.
Y. Huang, W. Wan, Y. Yang, C. Callison-Burch, M. Yatskar, and L. Liu, “Como: Controllable motion generation through language guided pose code editing,” in ECCV . Springer, 2024
2024
Closest in time.
OpenAI, “Chatgpt (mar 23 version),” https://chat.openai.com , 2023, accessed July 20, 2024
2024
Closest in time.