EchoStyle: Unlocking High-Fidelity Video Stylization with Reverse Data Synthesis

2026-06-24

AI Summary

Existing video stylization methods are often hindered by content leakage, limited training data, and poor adaptability to long videos, leading to style drift and motion distortion. To address these challenges, this work proposes a text-driven video-to-video generation framework that introduces a novel automated reverse synthesis pipeline to construct V-Style20k, a large-scale video stylization dataset. The approach further incorporates an initialization-following mechanism and a sliding-window inference strategy, enabling high-quality style transfer for videos of arbitrary length. Experimental results demonstrate that the proposed method achieves superior performance across diverse artistic styles, effectively mitigating style drift and motion artifacts while matching the quality of leading closed-source solutions.

Abstract

While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent content creation. Existing methods, usually utilizing a reference image as the style prior, suffer from content leakage, data scarcity and limited adaptability to long videos, leading to suboptimal results with severe style drift and motion distortion. For these issues, we present EchoStyle, a scalable text-driven framework to achieve high-quality stylization of videos with arbitrary lengths. To start with, we construct a video-to-video architecture to appropriately re-fuse the video content and the text style. To address data scarcity, we pioneer an automatic reverse-synthesis pipeline to establish V-Style20k, a large-scale stylization dataset of 20k high-quality video pairs. To facilitate long video stylization, we devise an init-follow-mode mechanism along with a sliding-window inference strategy. Extensive experiments demonstrate EchoStyle's excellent performance across a wide range of artistic styles, even comparable to leading closed-source solutions.

Problem

• video stylization
• style drift
• motion distortion
• data scarcity
• content leakage

Innovation

• reverse data synthesis
• text-driven video stylization
• long video stylization
• V-Style20k dataset
• init-follow-mode