2 citations · 2 across the 1 of their papers we have counts for
6 papers
FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis
Feng Liang, Bichen Wu, Jialiang Wang +8
Diffusion models have transformed the image-to-image (I2I) synthesis and are now permeating into videos. However, the advancement of video-to-video (V2V) synthesis has been hampere…
Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis
Bichen Wu, Ching-Yao Chuang, Xiaoyan Wang +6
In this paper, we introduce Fairy, a minimalist yet robust adaptation of image-editing diffusion models, enhancing them for video editing applications. Our approach centers on the…
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence
Yuchao Gu, Yipin Zhou, Bichen Wu +7
Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignmen…
MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers
Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu +5
Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly…
MixRT: Mixed Neural Representations For Real-Time NeRF Rendering
Chaojian Li, Bichen Wu, Peter Vajda +1
Neural Radiance Field (NeRF) has emerged as a leading technique for novel view synthesis, owing to its impressive photorealistic reconstruction and rendering capability. Neverthele…
AVID: Any-Length Video Inpainting with Diffusion Model
Zhixing Zhang, Bichen Wu, Xiaoyan Wang +6
Recent advances in diffusion models have successfully enabled text-guided image inpainting. While it seems straightforward to extend such editing capability into the video domain,…