Search Results for author:
Found 10 papers, 3 papers with code
I4VGen: Image as Stepping Stone for Text-to-Video Generation
Text-to-video generation has lagged behind text-to-image synthesis in quality and diversity due to the complexity of spatio-temporal modeling and limited video-text datasets.
Disentangling Foreground and Background Motion for Enhanced Realism in Human Video Generation
Training on real-world videos enhanced with this innovative motion depiction approach, our model generates videos exhibiting coherent movement in both foreground subjects and their surrounding contexts.
InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization
Recent strides in the development of diffusion models, exemplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visually compelling images.
DreaMoving: A Human Video Generation Framework based on Diffusion Models
In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human videos.
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models
This is a technical report on the 360-degree panoramic image generation task based on diffusion models.
Boosting3D: High-Fidelity Image-to-3D by Boosting 2D Diffusion Prior to 3D Prior with Progressive Learning
After the progressive training, the LoRA learns the 3D information of the generated object and eventually turns to an object-level 3D prior.
Adaptive Background Matting Using Background Matching
We use dynamic background video instead of static background for accurate matting.
Boosting Semantic Human Matting with Coarse Annotations
In this paper, we propose to use coarse annotated data coupled with fine annotated data to boost end-to-end semantic human matting without trimaps as extra input.
Ranked #9 on
Image Matting
on AM-2K
An Acceleration Framework for High Resolution Image Synthesis
The proposed acceleration framework makes it possible to generate high resolution images using less training time with limited hardware resource.
Question Answering based Clinical Text Structuring Using Pre-trained Language Model
Clinical text structuring is a critical and fundamental task for clinical research.
