12 citations · 20 across the 7 of their papers we have counts for
8 papers
A Video is Worth 256 Bases: Spatial-Temporal Expectation-Maximization Inversion for Zero-Shot Video Editing
Maomao Li, Yu Li, Tianyu Yang +4
This paper presents a video inversion approach for zero-shot video editing, which models the input video with low-rank representation during the inversion process. The existing vid…
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Maomao Li, Ge Yuan, Cairong Wang +5
This paper proposes a novel approach to face swapping from the perspective of fine-grained facial editing, dubbed "editing for swapping" (E4S). The traditional face swapping method…
ReliableSwap: Boosting General Face Swapping Via Reliable Supervision
Ge Yuan, Maomao Li, Yong Zhang +1
Almost all advanced face swapping approaches use reconstruction as the proxy task, i.e., supervision only exists when the target and source belong to the same person. Otherwise, la…
Inserting Anybody in Diffusion Models via Celeb Basis
Ge Yuan, Xiaodong Cun, Yong Zhang +5
Exquisite demand exists for customizing the pretrained large text-to-image model, , Stable Diffusion, to generate innovative concepts, such as the users themselves.…
Fine-Grained Face Swapping via Regional GAN Inversion
Zhian Liu, Maomao Li, Yong Zhang +4
We present a novel paradigm for high-fidelity face swapping that faithfully preserves the desired subtle geometry and texture details. We rethink face swapping from the perspective…
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
Yangtao Wang, Xi Shen, Yuan Yuan +5
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With…
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
Zhihui Lin, Tianyu Yang, Maomao Li +4
Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS). However, continu…
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
Shuangrui Ding, Maomao Li, Tianyu Yang +5
In light of the success of contrastive learning in the image domain, current self-supervised video representation learning methods usually employ contrastive loss to facilitate vid…