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pro vyhledávání: '"Yang, Xiaokang"'
Autor:
Xu, Liang, Hua, Shaoyang, Lin, Zili, Liu, Yifan, Ma, Feipeng, Yan, Yichao, Jin, Xin, Yang, Xiaokang, Zeng, Wenjun
In this paper, we tackle the problem of how to build and benchmark a large motion model (LMM). The ultimate goal of LMM is to serve as a foundation model for versatile motion-related tasks, e.g., human motion generation, with interpretability and gen
Externí odkaz:
http://arxiv.org/abs/2410.13790
While humans effortlessly discern intrinsic dynamics and adapt to new scenarios, modern AI systems often struggle. Current methods for visual grounding of dynamics either use pure neural-network-based simulators (black box), which may violate physica
Externí odkaz:
http://arxiv.org/abs/2410.08257
Autor:
Gong, Junchao, Tu, Siwei, Yang, Weidong, Fei, Ben, Chen, Kun, Zhang, Wenlong, Yang, Xiaokang, Ouyang, Wanli, Bai, Lei
Precipitation nowcasting plays a pivotal role in socioeconomic sectors, especially in severe convective weather warnings. Although notable progress has been achieved by approaches mining the spatiotemporal correlations with deep learning, these metho
Externí odkaz:
http://arxiv.org/abs/2410.05805
In the realm of computational physics, an enduring topic is the numerical solutions to partial differential equations (PDEs). Recently, the attention of researchers has shifted towards Neural Operator methods, renowned for their capability to approxi
Externí odkaz:
http://arxiv.org/abs/2410.05894
In the field of image editing, three core challenges persist: controllability, background preservation, and efficiency. Inversion-based methods rely on time-consuming optimization to preserve the features of the initial images, which results in low e
Externí odkaz:
http://arxiv.org/abs/2410.04844
Autor:
Chen, Zhuo, Yan, Yichao, Liu, Sehngqi, Cheng, Yuhao, Zhao, Weiming, Li, Lincheng, Bi, Mengxiao, Yang, Xiaokang
3D face editing is a significant task in multimedia, aimed at the manipulation of 3D face models across various control signals. The success of 3D-aware GAN provides expressive 3D models learned from 2D single-view images only, encouraging researcher
Externí odkaz:
http://arxiv.org/abs/2410.04965
Autor:
Li, Jianze, Cao, Jiezhang, Zou, Zichen, Su, Xiongfei, Yuan, Xin, Zhang, Yulun, Guo, Yong, Yang, Xiaokang
Diffusion models have been achieving excellent performance for real-world image super-resolution (Real-ISR) with considerable computational costs. Current approaches are trying to derive one-step diffusion models from multi-step counterparts through
Externí odkaz:
http://arxiv.org/abs/2410.04224
Training visual reinforcement learning agents in a high-dimensional open world presents significant challenges. While various model-based methods have improved sample efficiency by learning interactive world models, these agents tend to be "short-sig
Externí odkaz:
http://arxiv.org/abs/2410.03618
Graph neural networks have emerged as a powerful tool for large-scale mesh-based physics simulation. Existing approaches primarily employ hierarchical, multi-scale message passing to capture long-range dependencies within the graph. However, these gr
Externí odkaz:
http://arxiv.org/abs/2410.03779
Autor:
Li, Zhiteng, Yan, Xianglong, Zhang, Tianao, Qin, Haotong, Xie, Dong, Tian, Jiang, shi, zhongchao, Kong, Linghe, Zhang, Yulun, Yang, Xiaokang
Large Language Models (LLMs) have greatly pushed forward advancements in natural language processing, yet their high memory and computational demands hinder practical deployment. Binarization, as an effective compression technique, can shrink model w
Externí odkaz:
http://arxiv.org/abs/2410.03129