Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Xie, Haozhe"'
LiDAR scene generation has been developing rapidly recently. However, existing methods primarily focus on generating static and single-frame scenes, overlooking the inherently dynamic nature of real-world driving environments. In this work, we introd
Externí odkaz:
http://arxiv.org/abs/2410.18084
Autor:
Chen, Zhaoxi, Tang, Jiaxiang, Dong, Yuhao, Cao, Ziang, Hong, Fangzhou, Lan, Yushi, Wang, Tengfei, Xie, Haozhe, Wu, Tong, Saito, Shunsuke, Pan, Liang, Lin, Dahua, Liu, Ziwei
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed,
Externí odkaz:
http://arxiv.org/abs/2409.12957
Crowd Motion Generation is essential in entertainment industries such as animation and games as well as in strategic fields like urban simulation and planning. This new task requires an intricate integration of control and generation to realistically
Externí odkaz:
http://arxiv.org/abs/2407.06188
Video deblurring relies on leveraging information from other frames in the video sequence to restore the blurred regions in the current frame. Mainstream approaches employ bidirectional feature propagation, spatio-temporal transformers, or a combinat
Externí odkaz:
http://arxiv.org/abs/2406.07551
3D city generation with NeRF-based methods shows promising generation results but is computationally inefficient. Recently 3D Gaussian Splatting (3D-GS) has emerged as a highly efficient alternative for object-level 3D generation. However, adapting 3
Externí odkaz:
http://arxiv.org/abs/2406.06526
3D city generation is a desirable yet challenging task, since humans are more sensitive to structural distortions in urban environments. Additionally, generating 3D cities is more complex than 3D natural scenes since buildings, as objects of the same
Externí odkaz:
http://arxiv.org/abs/2309.00610
The key success factor of the video deblurring methods is to compensate for the blurry pixels of the mid-frame with the sharp pixels of the adjacent video frames. Therefore, mainstream methods align the adjacent frames based on the estimated optical
Externí odkaz:
http://arxiv.org/abs/2207.10852
Publikováno v:
In International Communications in Heat and Mass Transfer November 2024 158
Publikováno v:
In Energy 30 December 2024 313
Publikováno v:
ACM International Conference on Multimedia (ACM MM) 2021
Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknown regions according to the similarity between them. However, we find t
Externí odkaz:
http://arxiv.org/abs/2109.12252