VideoPainter introduces a plug‑and‑play dual‑branch framework for video inpainting and editing, featuring a lightweight context encoder, ID‑consistent resampling, and the large VPData/VPBench datasets, and achieves state‑of‑the‑art results across eight quantitative and qualitative metrics.
VideoPainter introduces a plug‑and‑play dual‑branch framework with a lightweight context encoder and ID‑resampling adapter, built on the massive VPData/VPBench dataset, and demonstrates state‑of‑the‑art performance across eight video restoration and editing metrics, while supporting flexible model integration and long‑video consistency.
The article introduces DeViT, a novel Deformed Vision Transformer framework for video inpainting that leverages a deformable patch homography estimator, mask‑pruned attention, and spatio‑temporal weight adaptation, achieving state‑of‑the‑art results on benchmark datasets and highlighting its potential for advanced video editing tools.
This article reviews the challenges of video inpainting, surveys traditional methods, and introduces the Spatial‑Temporal Transformer Network (STTN) that leverages multi‑scale attention and a Temporal Patch‑GAN discriminator, detailing its architecture, loss functions, training on Youtube‑VOS, and impressive restoration results.
At ACM MM 2021, Alibaba’s Moku Lab presented four cutting‑edge studies: an interactive video inpainting system using user doodles, a decoupled IoU regression model for object detection, a spatio‑temporal distortion‑aware video quality assessment framework, and a multimodal emotional relationship recognition dataset and benchmark.
