Video-to-Video Synthesis

8 papers with code • 2 benchmarks • 1 datasets

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Latest papers with no code

MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy

no code yet • 3 Apr 2024

We demonstrate that mesh stylization is a promising approach for creating realistic simulations for downstream tasks such as training and preoperative planning.

Translation-based Video-to-Video Synthesis

no code yet • 3 Apr 2024

Translation-based Video Synthesis (TVS) has emerged as a vital research area in computer vision, aiming to facilitate the transformation of videos between distinct domains while preserving both temporal continuity and underlying content features.

FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis

no code yet • 29 Dec 2023

This enables our model for video synthesis by editing the first frame with any prevalent I2I models and then propagating edits to successive frames.

Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis

no code yet • 20 Dec 2023

In this paper, we introduce Fairy, a minimalist yet robust adaptation of image-editing diffusion models, enhancing them for video editing applications.

Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets

no code yet • 14 Nov 2021

To corroborate the effectiveness of the proposed method, we evaluate the video classification task by comparing our dynamic cropping technique with random cropping on three benchmark datasets, viz.

World-Consistent Video-to-Video Synthesis

no code yet • ECCV 2020

This is because they lack knowledge of the 3D world being rendered and generate each frame only based on the past few frames.

ReenactNet: Real-time Full Head Reenactment

no code yet • 22 May 2020

Video-to-video synthesis is a challenging problem aiming at learning a translation function between a sequence of semantic maps and a photo-realistic video depicting the characteristics of a driving video.

Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching

no code yet • 27 Sep 2018

We study the joint distribution matching problem which aims at learning bidirectional mappings to match the joint distribution of two domains.