9 citations · 9 across the 1 of their papers we have counts for
6 papers · 1 filter
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
Yaohui Wang, Xinyuan Chen, Xin Ma +17
This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challengi…
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Yi Wang, Yinan He, Yizhuo Li +13
This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understand…
Weakly Supervised Scene Text Generation for Low-resource Languages
Yangchen Xie, Xinyuan Chen, Hongjian Zhan +4
A large number of annotated training images is crucial for training successful scene text recognition models. However, collecting sufficient datasets can be a labor-intensive and c…
LEO: Generative Latent Image Animator for Human Video Synthesis
Yaohui Wang, Xin Ma, Xinyuan Chen +4
Spatio-temporal coherency is a major challenge in synthesizing high quality videos, particularly in synthesizing human videos that contain rich global and local deformations. To re…
Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation
Zeyu Lu, Chengyue Wu, Xinyuan Chen +4
Diffusion models have attained impressive visual quality for image synthesis. However, how to interpret and manipulate the latent space of diffusion models has not been extensively…
DG-Font: Deformable Generative Networks for Unsupervised Font Generation
Yangchen Xie, Xinyuan Chen, Li Sun +1
Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years. Howe…