3 citations · 4 across the 28 of their papers we have counts for
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WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models
Zijian He, Peixin Chen, Guangrun Wang +3
Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person's pose and body shape in source videos. Traditional image-based metho…
AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment
Yuanfeng Xu, Yuhao Chen, Zhongzhan Huang +4
Recent video editing advancements rely on accurate pose sequences to animate subjects. However, these efforts are not suitable for cross-species animation due to pose misalignment…
GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing
Jing Wu, Jia-Wang Bian, Xinghui Li +4
We propose GaussCtrl, a text-driven method to edit a 3D scene reconstructed by the 3D Gaussian Splatting (3DGS). Our method first renders a collection of images by using the 3DGS a…
Geometry aware 3D generation from in-the-wild images in ImageNet
Qijia Shen, Guangrun Wang
Generating accurate 3D models is a challenging problem that traditionally requires explicit learning from 3D datasets using supervised learning. Although recent advances have shown…