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We present C$\cdot$ASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters.
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Catch & Carry: reusable neural controllers for vision-guided whole-body tasks
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MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
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ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
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Physics-based character controllers using conditional VAEs
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2022 · 2022
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ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
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Jiefeng Li, Siyuan Bian, Qi Liu, Jiasheng Tang, Fan Wang, and Cewu Lu. 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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CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, and Xue Bin Peng. 2023 · 2023
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Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction
Wenjia Wang, Yongtao Ge, Haiyi Mei, Zhongang Cai, Qingping Sun, Yanjun Wang, Chunhua Shen, Lei Yang, and Taku Komura. 2023 · 2023
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