Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Li, Peizhuo"'
We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike existing methods using an overly sparse high-dimensional latent, we p
Externí odkaz:
http://arxiv.org/abs/2407.18946
Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect to a fixed parametric human bo
Externí odkaz:
http://arxiv.org/abs/2407.06101
Autor:
Sun, Ge, Shafiee, Milad, Li, Peizhuo, Bellegarda, Guillaume, Ijspeert, Auke, Sartoretti, Guillaume
Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired con
Externí odkaz:
http://arxiv.org/abs/2404.17815
Creating believable motions for various characters has long been a goal in computer graphics. Current learning-based motion synthesis methods depend on extensive motion datasets, which are often challenging, if not impossible, to obtain. On the other
Externí odkaz:
http://arxiv.org/abs/2310.20249
Optimal Control for legged robots has gone through a paradigm shift from position-based to torque-based control, owing to the latter's compliant and robust nature. In parallel to this shift, the community has also turned to Deep Reinforcement Learnin
Externí odkaz:
http://arxiv.org/abs/2310.05714
We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual a
Externí odkaz:
http://arxiv.org/abs/2306.00378
Autor:
Raab, Sigal, Leibovitch, Inbal, Li, Peizhuo, Aberman, Kfir, Sorkine-Hornung, Olga, Cohen-Or, Daniel
The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains challenging, especially when the motions are highly diverse. In this work, we pre
Externí odkaz:
http://arxiv.org/abs/2206.08010
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and div
Externí odkaz:
http://arxiv.org/abs/2205.02625
Autor:
Yang, Qiang, Wei, Xiaohan, Hu, Tengfei, Wang, Jie, Li, Peizhuo, Gao, Aili, Wang, Jinlong, Cheng, Lihua, Huang, Shujuan, Bi, Xuejun
Publikováno v:
In Journal of Water Process Engineering September 2024 66
Autor:
Zihao, Li, Wang, Jinlong, Cheng, Lihua, Yang, Qiang, Li, Peizhuo, Dong, Xiaowan, Xu, Boyan, Zhi, Mei, Hao, Anni, Ng, How yong, Bi, Xuejun
Publikováno v:
In Water Research 1 September 2024 261