# TRACE: Trajectory Synchronization for Training-Free Resolution Extension in Video Generation

**Authors:** Libo Zhu, Fan Li, Zhixin Wang, Binglu Wang, Weisheng Dong, Yulun Zhang  
**Published:** 11 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.15983) · [PDF](https://arxiv.org/pdf/2611.15983) · [Code](https://github.com/libozhu03/TRACE) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.15983.html)

## Abstract

More text-to-video models are available, but many remain limited to low resolutions. Training them for higher resolutions is costly, while existing training-free methods still struggle with fine detail, scene structure, and efficiency. We propose TRACE, a training-free method that synchronizes low- and high-resolution generation trajectories. During early denoising, TRACE aligns predicted scene content at corresponding noise levels. It computes high-resolution updates from low-resolution predictions and the current high-resolution state, without high-resolution model inference. The high-resolution trajectory retains fine-grained variation for generating new details. Local window attention then refines fine details in a shared high-resolution latent state. TRACE checks suspected duplicate subjects against the corresponding low-resolution layout and corrects selected regions and their solver history while preserving detail elsewhere. Experiments across all kinds of video generation backbones and resolutions show improved perceptual quality and text-video alignment over FreeSwim, a state-of-the-art training-free method, with 2.0-2.9x end-to-end speedups. Our code and models will be released at https://github.com/libozhu03/TRACE.

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