Fetching the paper…
Reading the bibliography…
The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently explored in the motion domain.
Factored conditional restricted Boltzmann machines for modeling motion style. In Proceedings of the 26th annual international conference on machine learning . 1025–1032
Graham W Taylor and Geoffrey E Hinton. 2009 · 2009
Earlier work this paper cites.
Generating text with recurrent neural networks. In ICML
Ilya Sutskever, James Martens, and Geoffrey E Hinton. 2011 · 2011
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra. 2014 · 2014
Earlier work this paper cites.
Recurrent network models for human dynamics. In Proceedings of the IEEE International Conference on Computer Vision . 4346–4354
Katerina Fragkiadaki, Sergey Levine, Panna Felsen, and Jitendra Malik. 2015 · 2015
Earlier work this paper cites.
Draw: A recurrent neural network for image generation. In International conference on machine learning . PMLR, 1462–1471
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra. 2015 · 2015
Earlier work this paper cites.
Learning motion manifolds with convolutional autoencoders
Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning . PMLR, 2256–2265
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms. In International conference on machine learning . PMLR, 843–852
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov. 2015 · 2015
Earlier work this paper cites.
Show and tell: A neural image caption generator. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3156–3164
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2015 · 2015
Earlier work this paper cites.
A deep learning framework for character motion synthesis and editing
Daniel Holden, Jun Saito, and Taku Komura. 2016 · 2016
Earlier work this paper cites.
Phase-functioned neural networks for character control
Daniel Holden, Taku Komura, and Jun Saito. 2017 · 2017
Earlier work this paper cites.
Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms
Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu. 2017 · 2017
Earlier work this paper cites.
Interactive character animation by learning multi-objective control
Kyungho Lee, Seyoung Lee, and Jehee Lee. 2018 · 2018
Earlier work this paper cites.
Quaternet: A quaternion-based recurrent model for human motion
Dario Pavllo, David Grangier, and Michael Auli. 2018 · 2018
Earlier work this paper cites.
Neural kinematic networks for unsupervised motion retargetting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 8639–8648
Ruben Villegas, Jimei Yang, Duygu Ceylan, and Honglak Lee. 2018 · 2018
Earlier work this paper cites.
Mode-adaptive neural networks for quadruped motion control
He Zhang, Sebastian Starke, Taku Komura, and Jun Saito. 2018 · 2018
Earlier work this paper cites.
Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, and Hao Li. 2018 · 2018
Cited alongside, same era.
Learning Character-Agnostic Motion for Motion Retargeting in 2D
Kfir Aberman, Rundi Wu, Dani Lischinski, Baoquan Chen, and Daniel Cohen-Or. 2019 · 2019
Cited alongside, same era.
On the continuity of rotation representations in neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5745–5753
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. 2019 · 2019
Cited alongside, same era.
Skeleton-aware networks for deep motion retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski, Olga Sorkine-Hornung, Daniel Cohen-Or, and Baoquan Chen. 2020a · 2020
Cited alongside, same era.
Unpaired motion style transfer from video to animation
Kfir Aberman, Yijia Weng, Dani Lischinski, Daniel Cohen-Or, and Baoquan Chen. 2020b · 2020
High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2021 · 2021
Later among the works it cites.
Neural animation layering for synthesizing martial arts movements
Sebastian Starke, Yiwei Zhao, Fabio Zinno, and Taku Komura. 2021 · 2021
Later among the works it cites.
Prompt-to-prompt image editing with cross attention control
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. 2022 · 2022
Later among the works it cites.
Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. 2022 · 2022
Later among the works it cites.
Flame: Free-form language-based motion synthesis & editing
Jihoon Kim, Jiseob Kim, and Sungjoon Choi. 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…
Cited alongside, same era.
Robust motion in-betweening
Félix G Harvey, Mike Yurick, Derek Nowrouzezahrai, and Christopher Pal. 2020 · 2020
Cited alongside, same era.
Moglow: Probabilistic and controllable motion synthesis using normalising flows
Gustav Eje Henter, Simon Alexanderson, and Jonas Beskow. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
Learned motion matching
Daniel Holden, Oussama Kanoun, Maksym Perepichka, and Tiberiu Popa. 2020 · 2020
Cited alongside, same era.
Character controllers using motion VAEs
Hung Yu Ling, Fabio Zinno, George Cheng, and Michiel Van De Panne. 2020 · 2020
Cited alongside, same era.
Denoising Diffusion Implicit Models. In International Conference on Learning Representations
Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020 · 2020
Cited alongside, same era.
Local motion phases for learning multi-contact character movements
Sebastian Starke, Yiwei Zhao, Taku Komura, and Kazi Zaman. 2020 · 2020
Cited alongside, same era.
Ganimator: Neural motion synthesis from a single sequence
Peizhuo Li, Kfir Aberman, Zihan Zhang, Rana Hanocka, and Olga Sorkine-Hornung. 2022 · 2022
Later among the works it cites.
Ian Mason, Sebastian Starke, and Taku Komura. 2022 · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2022 · 2022
Later among the works it cites.
Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings . 1–10
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. 2022a · 2022
Later among the works it cites.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. 2022b · 2022
Later among the works it cites.
Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. 2022 · 2022
Later among the works it cites.
Motiondiffuse: Text-driven human motion generation with diffusion model
Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. 2022 · 2022
Later among the works it cites.
Human Motion Diffusion as a Generative Prior
Yonatan Shafir, Guy Tevet, Roy Kapon, and Amit H. Bermano. 2023 · 2023
Closest in time.