Zobrazeno 1 - 10
of 2 458
pro vyhledávání: '"Tian, Zheng"'
In computer animation, driving a simulated character with lifelike motion is challenging. Current generative models, though able to generalize to diverse motions, often pose challenges to the responsiveness of end-user control. To address these issue
Externí odkaz:
http://arxiv.org/abs/2406.17795
Diffusion models have achieved great success in image generation. However, when leveraging this idea for video generation, we face significant challenges in maintaining the consistency and continuity across video frames. This is mainly caused by the
Externí odkaz:
http://arxiv.org/abs/2403.13408
Information retrieval is an ever-evolving and crucial research domain. The substantial demand for high-quality human motion data especially in online acquirement has led to a surge in human motion research works. Prior works have mainly concentrated
Externí odkaz:
http://arxiv.org/abs/2403.00691
Detecting whether copyright holders' works were used in LLM pretraining is poised to be an important problem. This work proposes using data watermarks to enable principled detection with only black-box model access, provided that the rightholder cont
Externí odkaz:
http://arxiv.org/abs/2402.10892
Autor:
Zu, Weiqin, Song, Wenbin, Chen, Ruiqing, Guo, Ze, Sun, Fanglei, Tian, Zheng, Pan, Wei, Wang, Jun
The socially-aware navigation system has evolved to adeptly avoid various obstacles while performing multiple tasks, such as point-to-point navigation, human-following, and -guiding. However, a prominent gap persists: in Human-Robot Interaction (HRI)
Externí odkaz:
http://arxiv.org/abs/2311.08244
Autor:
Chen, Mingcheng, Zhao, Haoran, Zhao, Yuxiang, Fan, Hulei, Gao, Hongqiao, Yu, Yong, Tian, Zheng
Data-driven black-box model-based optimization (MBO) problems arise in a great number of practical application scenarios, where the goal is to find a design over the whole space maximizing a black-box target function based on a static offline dataset
Externí odkaz:
http://arxiv.org/abs/2310.07560
Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper, recent years have witnessed an increasing focus on its multi-agent nature by researchers utilizing it as a testbed f
Externí odkaz:
http://arxiv.org/abs/2309.12951
Autor:
Li, Yang, Yu, Cheng, Sun, Guangzhi, Zu, Weiqin, Tian, Zheng, Wen, Ying, Pan, Wei, Zhang, Chao, Wang, Jun, Yang, Yang, Sun, Fanglei
Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoenco
Externí odkaz:
http://arxiv.org/abs/2309.04156
The Digital Services Act, recently adopted by the EU, requires social media platforms to report the "accuracy" of their automated content moderation systems. The colloquial term is vague, or open-textured -- the literal accuracy (number of correct pr
Externí odkaz:
http://arxiv.org/abs/2305.09601
Autor:
Song, Yan, Jiang, He, Tian, Zheng, Zhang, Haifeng, Zhang, Yingping, Zhu, Jiangcheng, Dai, Zonghong, Zhang, Weinan, Wang, Jun
Publikováno v:
Machine Intelligence Research (2024)
Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public. In this w
Externí odkaz:
http://arxiv.org/abs/2305.09458