Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Dai, Zuozhuo"'
Autor:
Kong, Weijie, Tian, Qi, Zhang, Zijian, Min, Rox, Dai, Zuozhuo, Zhou, Jin, Xiong, Jiangfeng, Li, Xin, Wu, Bo, Zhang, Jianwei, Wu, Kathrina, Lin, Qin, Yuan, Junkun, Long, Yanxin, Wang, Aladdin, Wang, Andong, Li, Changlin, Huang, Duojun, Yang, Fang, Tan, Hao, Wang, Hongmei, Song, Jacob, Bai, Jiawang, Wu, Jianbing, Xue, Jinbao, Wang, Joey, Wang, Kai, Liu, Mengyang, Li, Pengyu, Li, Shuai, Wang, Weiyan, Yu, Wenqing, Deng, Xinchi, Li, Yang, Chen, Yi, Cui, Yutao, Peng, Yuanbo, Yu, Zhentao, He, Zhiyu, Xu, Zhiyong, Zhou, Zixiang, Xu, Zunnan, Tao, Yangyu, Lu, Qinglin, Liu, Songtao, Zhou, Daquan, Wang, Hongfa, Yang, Yong, Wang, Di, Liu, Yuhong, Jiang, Jie, Zhong, Caesar
Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilitie
Externí odkaz:
http://arxiv.org/abs/2412.03603
Autor:
Zhang, Zhenghao, Liao, Junchao, Li, Menghao, Dai, Zuozhuo, Qiu, Bingxue, Zhu, Siyu, Qin, Long, Wang, Weizhi
Recent advancements in Diffusion Transformer (DiT) have demonstrated remarkable proficiency in producing high-quality video content. Nonetheless, the potential of transformer-based diffusion models for effectively generating videos with controllable
Externí odkaz:
http://arxiv.org/abs/2407.21705
Autor:
Zhu, Shenhao, Chen, Junming Leo, Dai, Zuozhuo, Su, Qingkun, Xu, Yinghui, Cao, Xun, Yao, Yao, Zhu, Hao, Zhu, Siyu
In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques. The methodology u
Externí odkaz:
http://arxiv.org/abs/2403.14781
Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets. Current video editing methods commonly require per-video fine-tuning of diffusion m
Externí odkaz:
http://arxiv.org/abs/2403.11568
We introduce Gaussian-Flow, a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the prevalent NeRF-based approaches hampered by slow training and renderi
Externí odkaz:
http://arxiv.org/abs/2312.03431
Image animation is a key task in computer vision which aims to generate dynamic visual content from static image. Recent image animation methods employ neural based rendering technique to generate realistic animations. Despite these advancements, ach
Externí odkaz:
http://arxiv.org/abs/2311.12886
State-of-the-art text-video retrieval (TVR) methods typically utilize CLIP and cosine similarity for efficient retrieval. Meanwhile, cross attention methods, which employ a transformer decoder to compute attention between each text query and all fram
Externí odkaz:
http://arxiv.org/abs/2307.09972
The current state-of-the-art methods for unsupervised video object segmentation (UVOS) require extensive training on video datasets with mask annotations, limiting their effectiveness in handling challenging scenarios. However, the Segment Anything M
Externí odkaz:
http://arxiv.org/abs/2305.12659
Most existing transformer based video instance segmentation methods extract per frame features independently, hence it is challenging to solve the appearance deformation problem. In this paper, we observe the temporal information is important as well
Externí odkaz:
http://arxiv.org/abs/2301.09416
3D motion estimation including scene flow and point cloud registration has drawn increasing interest. Inspired by 2D flow estimation, recent methods employ deep neural networks to construct the cost volume for estimating accurate 3D flow. However, th
Externí odkaz:
http://arxiv.org/abs/2205.11028