WANDR: Intention-guided Human Motion Generation
Markos Diomataris, Nikos Athanasiou, Omid Taheri, Xi Wang, Otmar Hilliges, Michael J. Black
2024Year
10Top-tier citations
Abstract
a conditional Variational Auto-Encoder (c-VAE), which we train using the AMASS and CIRCLE datasets. We evaluate our method extensively and demonstrate its ability to generate natural and long-term motions that reach 3D goals and generalize to unseen goal locations. Our models and code are available for research purposes at wandr.is.tue.mpg.de.
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Install the CLIlune papers fulltext 3529ce8d-53f7-4e53-9188-fb33888da52cCited by top-tier papers10
- MotionHiFlow: Text-to-Motion via Hierarchical Flow MatchingHeng Li, Xiaotong Lin, Ling-An Zeng, Yulei Kang et al.CVPR 2026 · 7 citations
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Builds on15
- AMASS: Archive of Motion Capture As Surface ShapesNaureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll et al.ICCV 2019 · 1,784 citations
- Action-Conditioned 3D Human Motion Synthesis with Transformer VAEMathis Petrovich, Michael J. Black, Gül VarolICCV 2021 · 672 citations
- HuMoR: 3D Human Motion Model for Robust Pose EstimationDavis Rempe, Tolga Birdal, Aaron Hertzmann, Jimei Yang et al.ICCV 2021 · 398 citations
- AMP: adversarial motion priors for stylized physics-based character controlXue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine et al.SIGGRAPH 2021 · 392 citations
- Stochastic Scene-Aware Motion PredictionMohamed Hassan, Duygu Ceylan, Ruben Villegas, Jun Saito et al.ICCV 2021 · 240 citations
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