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Found 9 papers, 6 papers with code
A Video is Worth 256 Bases: Spatial-Temporal Expectation-Maximization Inversion for Zero-Shot Video Editing
This paper presents a video inversion approach for zero-shot video editing, which aims to model the input video with low-rank representation during the inversion process.
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Based on this disentanglement, face swapping can be simplified as style and mask swapping.
ReliableSwap: Boosting General Face Swapping Via Reliable Supervision
To avoid the potential artifacts and drive the distribution of the network output close to the natural one, we reversely take synthetic images as input while the real face as reliable supervision during the training stage of face swapping.
Inserting Anybody in Diffusion Models via Celeb Basis
Empowered by the proposed celeb basis, the new identity in our customized model showcases a better concept combination ability than previous personalization methods.
Fine-Grained Face Swapping via Regional GAN Inversion
We rethink face swapping from the perspective of fine-grained face editing, \textit{i. e., ``editing for swapping'' (E4S)}, and propose a framework that is based on the explicit disentanglement of the shape and texture of facial components.
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
Ranked #4 on
Unsupervised Instance Segmentation
on COCO val2017
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS).
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
To alleviate such bias, we propose \textbf{F}oreground-b\textbf{a}ckground \textbf{Me}rging (FAME) to deliberately compose the moving foreground region of the selected video onto the static background of others.
Self-Attention ConvLSTM for Spatiotemporal Prediction
To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM.
Ranked #23 on
Video Prediction
on Moving MNIST
