Fetching the paper…
Reading the bibliography…
Controllable generation of 3D human motions becomes an important topic as the world embraces digital transformation.
High-Fidelity Image Generation With Fewer Labels
Lucic, M.; Tschannen, M.; Ritter, M.; Zhai, X.; Bachem, O.; and Gelly, S. 2019 · 1903
Earlier work this paper cites.
Jukebox: A Generative Model for Music
Dhariwal, P.; Jun, H.; Payne, C.; Kim, J. W.; Radford, A.; and Sutskever, I. 2020 · 2005
Earlier work this paper cites.
Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2006
Earlier work this paper cites.
Monocular Expressive Body Regression through Body-Driven Attention
Choutas, V.; Pavlakos, G.; Bolkart, T.; Tzionas, D.; and Black, M. J. 2020 · 2008
Earlier work this paper cites.
Learning to Generate Diverse Dance Motions with Transformer
Li, J.; Yin, Y.; Chu, H.; Zhou, Y.; Wang, T.; Fidler, S.; and Li, H. 2020 · 2008
Earlier work this paper cites.
Semi-supervised Learning with Deep Generative Models
Kingma, D. P.; Mohamed, S.; Rezende, D. J.; and Welling, M. 2014 · 2014
Earlier work this paper cites.
SMPL: a skinned multi-person linear model
Loper, M.; Mahmood, N.; Romero, J.; Pons-Moll, G.; and Black, M. J. 2015 · 2015
Earlier work this paper cites.
The KIT Motion-Language Dataset
Plappert, M.; Mandery, C.; and Asfour, T. 2016 · 2016
Earlier work this paper cites.
Triple Generative Adversarial Nets
Li, C.; Xu, T.; Zhu, J.; and Zhang, B. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need
Vaswani, A.; Shazeer, N. M.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
A Large-scale RGB-D Database for Arbitrary-view Human Action Recognition
Ji, Y.; Xu, F.; Yang, Y.; Shen, F.; Shen, H. T.; and Zheng, W. 2018 · 2018
Earlier work this paper cites.
End-to-End Recovery of Human Shape and Pose
Kanazawa, A.; Black, M. J.; Jacobs, D. W.; and Malik, J. 2017 · 2018
Earlier work this paper cites.
Language2Pose: Natural Language Grounded Pose Forecasting
Ahuja, C.; and Morency, L.-P. 2019 · 2019
Earlier work this paper cites.
AMASS: Archive of Motion Capture As Surface Shapes
Mahmood, N.; Ghorbani, N.; Troje, N. F.; Pons-Moll, G.; and Black, M. J. 2019 · 2019
Earlier work this paper cites.
Human Motion Prediction via Spatio-Temporal Inpainting
Ruiz, A. H.; Gall, J.; and Moreno-Noguer, F. 2018 · 2019
Earlier work this paper cites.
On the Continuity of Rotation Representations in Neural Networks
Zhou, Y.; Barnes, C.; Lu, J.; Yang, J.; and Li, H. 2018 · 2019
Earlier work this paper cites.
Action2Motion: Conditioned Generation of 3D Human Motions
Guo, C.; Zuo, X.; Wang, S.; Zou, S.; Sun, Q.; Deng, A.; Gong, M.; and Cheng, L. 2020 · 2020
Earlier work this paper cites.
VIBE: Video Inference for Human Body Pose and Shape Estimation
Kocabas, M.; Athanasiou, N.; and Black, M. J. 2019 · 2020
Earlier work this paper cites.
Character controllers using motion VAEs
Ling, H. Y.; Zinno, F.; Cheng, G.; and van de Panne, M. 2020 · 2020
Cited alongside, same era.
Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure
Aristidou, A.; Yiannakidis, A.; Aberman, K.; Cohen-Or, D.; Shamir, A.; and Chrysanthou, Y. 2021 · 2021
Cited alongside, same era.
Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents
Bhattacharya, U.; Rewkowski, N.; Banerjee, A.; Guhan, P.; Bera, A.; and Manocha, D. 2021 · 2021
Cited alongside, same era.
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
Cited alongside, same era.
Synthesis of Compositional Animations from Textual Descriptions
Ghosh, A.; Cheema, N.; Oguz, C.; Theobalt, C.; and Slusallek, P. 2021 · 2021
Cited alongside, same era.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Saharia, C.; Chan, W.; Saxena, S.; Li, L.; Whang, J.; Denton, E. L.; Ghasemipour, S. K. S.; Ayan, B. K.; Mahdavi, S. S.; Lopes, R. G.; Salimans, T.; Ho, J.; Fleet, D. J.; and Norouzi, M. 2022 · 2022
Later among the works it cites.
Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory
Siyao, L.; Yu, W.; Gu, T.; Lin, C.; Wang, Q.; Qian, C.; Loy, C. C.; and Liu, Z. 2022 · 2022
Later among the works it cites.
Tevet, G.; Raab, S.; Gordon, B.; Shafir, Y.; Cohen-Or, D.; and Bermano, A. H. 2022 · 2022
Later among the works it cites.
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields
Tiwari, G.; Antic, D.; Lenssen, J. E.; Sarafianos, N.; Tung, T.; and Pons-Moll, G. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lee, L.-H.; Braud, T.; Zhou, P.; Wang, L.; Xu, D.; Lin, Z.; Kumar, A.; Bermejo, C.; and Hui, P. 2021 · 2021
Cited alongside, same era.
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Li, R.; Yang, S.; Ross, D. A.; and Kanazawa, A. 2021 · 2021
Cited alongside, same era.
Action-Conditioned 3D Human Motion Synthesis with Transformer VAE
Petrovich, M.; Black, M. J.; and Varol, G. 2021 · 2021
Cited alongside, same era.
Why Are Conditional Generative Models Better Than Unconditional Ones?
Bao, F.; Li, C.; Sun, J.; and Zhu, J. 2022 · 2022
Cited alongside, same era.
Implicit Neural Representations for Variable Length Human Motion Generation
Cervantes, P.; Sekikawa, Y.; Sato, I.; and Shinoda, K. 2022 · 2022
Cited alongside, same era.
Executing your Commands via Motion Diffusion in Latent Space
Chen, X.; Jiang, B.; Liu, W.; Huang, Z.; Fu, B.; Chen, T.; Yu, J.; and Yu, G. 2022 · 2022
Cited alongside, same era.
Imagen Video: High Definition Video Generation with Diffusion Models
Ho, J.; Chan, W.; Saharia, C.; Whang, J.; Gao, R.; Gritsenko, A. A.; Kingma, D. P.; Poole, B.; Norouzi, M.; Fleet, D. J.; and Salimans, T. 2022 · 2022
Cited alongside, same era.
Tseng, J.-H.; Castellon, R.; and Liu, C. K. 2022 · 2022
Later among the works it cites.
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
Zhang, M.; Cai, Z.; Pan, L.; Hong, F.; Guo, X.; Yang, L.; and Liu, Z. 2022 · 2022
Later among the works it cites.
Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation
Azadi, S.; Shah, A.; Hayes, T.; Parikh, D.; and Gupta, S. 2023 · 2023
Later among the works it cites.
On the Design Fundamentals of Diffusion Models: A Survey
Chang, Z.; Koulieris, G. A.; and Shum, H. P. H. 2023 · 2023
Later among the works it cites.
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
Daras, G.; Shah, K.; Dagan, Y.; Gollakota, A.; Dimakis, A. G.; and Klivans, A. R. 2023 · 2023
Later among the works it cites.
Motion-DVAE: Unsupervised learning for fast human motion denoising
Fiche, G.; Leglaive, S.; Alameda-Pineda, X.; and S’eguier, R. 2023 · 2023
Later among the works it cites.
TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
Gong, K.; Lian, D.; Chang, H.; Guo, C.; Zuo, X.; Jiang, Z.; and Wang, X. 2023 · 2023
Later among the works it cites.
Back to MLP: A Simple Baseline for Human Motion Prediction
Guo, W.; Du, Y.; Shen, X.; Lepetit, V.; Alameda-Pineda, X.; and Moreno-Noguer, F. 2022c · 2023
Later among the works it cites.
GSURE-Based Diffusion Model Training with Corrupted Data
Kawar, B.; Elata, N.; Michaeli, T.; and Elad, M. 2023 · 2023
Later among the works it cites.
DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration
Miao, Y.-C.; Zhang, L.; Zhang, L.; and Tao, D. 2023 · 2023
Later among the works it cites.
Exploring Diffusion Models for Unsupervised Video Anomaly Detection
Tur, A. O.; Dall’Asen, N.; Beyan, C.; and Ricci, E. 2023 · 2023
Later among the works it cites.
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
You, Z.; Zhong, Y.; Bao, F.; Sun, J.; Li, C.; and Zhu, J. 2023 · 2023
Later among the works it cites.
Anticipating human activities for reactive robotic response
Koppula, H. S.; and Saxena, A. 2013 · 2071
Closest in time.