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We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals.
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Amp: Adversarial motion priors for stylized physics-based character control
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Classifier-Free Diffusion Guidance
Jonathan Ho. 2022 · 2022
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Real-time style modelling of human locomotion via feature-wise transformations and local motion phases
Ian Mason, Sebastian Starke, and Taku Komura. 2022 · 2022
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Mofusion: A framework for denoising-diffusion-based motion synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9760–9770
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C· ase: Learning conditional adversarial skill embeddings for physics-based characters. In SIGGRAPH Asia 2023 Conference Papers . 1–11
Zhiyang Dou, Xuelin Chen, Qingnan Fan, Taku Komura, and Wenping Wang. 2023 · 2023
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Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen. 2023 · 2023
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. In International Conference on Machine Learning (ICML) . PMLR, 16784–16804
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. 2022 · 2022
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Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler. 2022 · 2022
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Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
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High-resolution image synthesis with latent diffusion models. In Conference on Computer Vision and Pattern Recognition (CVPR) . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
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Deepphase: Periodic autoencoders for learning motion phase manifolds
Sebastian Starke, Ian Mason, and Taku Komura. 2022 · 2022
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Physics-based character controllers using conditional vaes
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2022 · 2022
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Flame: Free-form language-based motion synthesis & editing. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 8255–8263
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Latent consistency models: Synthesizing high-resolution images with few-step inference
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. 2023 · 2023
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On distillation of guided diffusion models. In Conference on Computer Vision and Pattern Recognition (CVPR) . 14297–14306
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans. 2023 · 2023
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Human motion diffusion as a generative prior
Yonatan Shafir, Guy Tevet, Roy Kapon, and Amit H Bermano. 2023 · 2023
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Controllable Motion Diffusion Model
Yi Shi, Jingbo Wang, Xuekun Jiang, and Bo Dai. 2023 · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. 2023 · 2023
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Calm: Conditional adversarial latent models for directable virtual characters. In ACM SIGGRAPH 2023 Conference Proceedings . 1–9
Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, and Xue Bin Peng. 2023 · 2023
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Human motion diffusion model
Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. 2023 · 2023
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Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) . 22035–22044
Yin Wang, Zhiying Leng, Frederick W. B. Li, Shun-Cheng Wu, and Xiaohui Liang. 2023 · 2023
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Omnicontrol: Control any joint at any time for human motion generation
Yiming Xie, Varun Jampani, Lei Zhong, Deqing Sun, and Huaizu Jiang. 2023 · 2023
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Physdiff: Physics-guided human motion diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 16010–16021
Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz. 2023 · 2023
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AI4Animation
Sebastian Starke. 2024 · 2024
Closest in time.