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新论文 · v1arXiv:2611.15983

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

Libo Zhu, Fan Li, Zhixin Wang, Binglu Wang, Weisheng Dong, Yulun Zhang

原文摘要

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.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.29845

Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding

Zihan Zhou, Libo Zhu, Jue Gong, Zhiyi Zhou, Jiezhang Cao, Yong Guo, Yulun Zhang

原文摘要

Flicker-Banding (FB) arises from temporal aliasing between a camera's rolling shutter and a display's brightness modulation, degrading screen-captured image readability with color shifts and jagged patterns. Existing single-frame methods with simplified parametric stripe models cannot reliably distinguish these artifacts from genuine texture. To address this, we conduct a systematic analysis of complex FB morphologies and reveal their significant variation across exposure settings, motivating a multi-frame bracketed RAW restoration paradigm. We construct Bricker, a synthetic-real bracketed RAW dataset built via ray-tracing-based physical simulation and an automated multi-exposure capture tool. We further propose BRACE: Bracketed RAW Flicker-Banding Removal, a multi-frame restoration model that utilizes a frequency-aware banding prior and a multi-scale spatial cross-attention modulator (MSCAM) for cross-exposure spatial fusion. We also introduce the Stripe Frequency Consistency (SFC) metric to evaluate banding removal. Experiments demonstrate state-of-the-art performance on both synthetic and real benchmarks. Our dataset and code are available at: https://github.com/ZZH-qwq/BRACE

cs.CV

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2509.24644

RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement

Libo Zhu, Zihan Zhou, Xiaoyang Liu, Weihang Zhang, Keyu Shi, Yifan Fu, Yulun Zhang

原文摘要

Capturing screens is now routine in our everyday lives. But the photographs of emissive displays are often influenced by the flicker-banding (FB), which is alternating bright-dark stripes that arise from temporal aliasing between a camera's rolling-shutter readout and the display's brightness modulation. Unlike moire degradation, which has been extensively studied, the FB remains underexplored despite its frequent and severe impact on readability and perceived quality. We formulate FB removal as a dedicated restoration task and introduce Removal of Image Flicker-Banding via Latent Diffusion Enhancement, RIFLE, a diffusion-based framework designed to remove FB while preserving fine details. We propose the flicker-banding prior estimator (FPE) that predicts key banding attributes and injects it into the restoration network. Additionally, Masked Loss (ML) is proposed to concentrate supervision on banded regions without sacrificing global fidelity. To overcome data scarcity, we provide a simulation pipeline that synthesizes FB in the luminance domain with stochastic jitter in banding angle, banding spacing, and banding width. Feathered boundaries and sensor noise are also applied for a more realistic simulation. For evaluation, we collect a paired real-world FB dataset with pixel-aligned banding-free references captured via long exposure. Across quantitative metrics and visual comparisons on our real-world dataset, RIFLE consistently outperforms recent image reconstruction baselines from mild to severe flicker-banding. To the best of our knowledge, it is the first work to research the simulation and removal of FB. Our work establishes a great foundation for subsequent research in both the dataset construction and the removal model design. Our dataset and code will be released soon.

cs.CV

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arXiv ↗PDF ↗