113 citations · 337 across the 24 of their papers we have counts for
7 papers · 1 filter
VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models
Zhen Xing, Qi Dai, Shuyuan Tu +5
Diffusion models have achieved significant success in image and video generation. This motivates a growing interest in video editing tasks, where videos are edited according to pro…
ARTV: Auto-Regressive Text-to-Video Generation with Diffusion Models
Wenming Weng, Ruoyu Feng, Yanhui Wang +10
We present ARTV, an efficient framework for auto-regressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-s…
MotionEditor: Editing Video Motion via Content-Aware Diffusion
Shuyuan Tu, Qi Dai, Zhi-Qi Cheng +4
Existing diffusion-based video editing models have made gorgeous advances for editing attributes of a source video over time but struggle to manipulate the motion information while…
A Survey on Video Diffusion Models
Zhen Xing, Qijun Feng, Haoran Chen +5
The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to the…
SimDA: Simple Diffusion Adapter for Efficient Video Generation
Zhen Xing, Qi Dai, Han Hu +2
The recent wave of AI-generated content has witnessed the great development and success of Text-to-Image (T2I) technologies. By contrast, Text-to-Video (T2V) still falls short of e…
Implicit Temporal Modeling with Learnable Alignment for Video Recognition
Shuyuan Tu, Qi Dai, Zuxuan Wu +3
Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an o…
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
Jia Ning, Chen Li, Zheng Zhang +4
Unlike language tasks, where the output space is usually limited to a set of tokens, the output space of visual tasks is more complicated, making it difficult to build a unified vi…