MoFusion: A Framework for Denoising-Diffusion-Based Motion Synthesis
Rishabh Dabral, Muhammad Hamza Mughal, Vladislav Golyanik, Christian Theobalt
2023年份
99顶会引用
摘要
A person jumps multiple times A figure crawls on the floor A person kicks with right leg Walking counter-clockwise Figure 1. Our MoFusion approach synthesises long sequences of human motions in 3D from textual and audio inputs (e.g., by providing music samples). Our model has significantly improved generalisability and realism, and can be conditioned on modalities like text and audio. The resulting dance movements match the rhythm of the conditioning music, even if it is outside the training distribution.
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引用它的顶会 Paper99
- Human Motion Diffusion as a Generative PriorYoni Shafir, Guy Tevet, Roy Kapon, Amit Haim BermanoICLR 2024 · 被引用 371 次
- Guided Motion Diffusion for Controllable Human Motion SynthesisKorrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, Siyu TangICCV 2023 · 被引用 240 次
- InterDiff: Generating 3D Human-Object Interactions with Physics-Informed DiffusionSirui Xu, Zhengyuan Li, Yu-Xiong Wang, Liang-Yan GuiICCV 2023 · 被引用 201 次
- TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion SynthesisMathis Petrovich, Michael J. Black, Gül VarolICCV 2023 · 被引用 192 次
- Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion ModelsSimon Alexanderson, Rajmund Nagy, Jonas Beskow, Gustav Eje HenterSIGGRAPH 2023 · 被引用 191 次
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