ViBe: Ultra-High-Resolution Video Synthesis Born from Pure Images 🎥✨

Teaser: ViBe generates ultra-high-resolution videos with rich details and coherent global structure. ✨

Introduction 🎬

ViBe presents an image-only training framework for ultra-high-resolution video generation, aiming to unlock 4K video synthesis from pre-trained video Diffusion Transformers without expensive high-resolution video training. Instead of relying on large-scale high-resolution video data, ViBe explores how a low-resolution video generator can be effectively adapted using only image supervision.

A central challenge in this setting is the modality gap between images and videos. Directly fine-tuning a video model on high-resolution images often leads to artifacts and residual noise during video inference. To address this, ViBe introduces Relay LoRA, a two-stage adaptation strategy that separates modality alignment from spatial extrapolation, enabling high-resolution generation while reducing image-induced degradation.

To further improve generation quality, ViBe proposes Global-Coarse-Local-Fine (GCLF) Attention, which combines local fine-grained modeling with compact global context aggregation. This design improves detail fidelity while maintaining semantic consistency across the entire frame.

ViBe also introduces a High-Frequency-Awareness Training Objective (HFATO) to enhance the recovery of fine textures and sharp structures from degraded latent inputs. This leads to clearer and more detailed high-resolution video synthesis.

Extensive experiments on modern video DiTs, such as Wan2.2, show that ViBe can generate 4K videos with strong semantic coherence and rich visual details, achieving state-of-the-art performance on VBench.

Methodology 🧠

• Relay LoRA for Decoupled Adaptation: 🔄 We introduce Relay LoRA, a two-stage adaptation strategy that first bridges the image--video modality gap at low resolution and then learns spatial extrapolation at high resolution, enabling high-resolution video synthesis while avoiding image-induced artifacts.

• Global-Coarse-Local-Fine Attention: 🔍 We design GCLF Attention, which combines localized fine-grained modeling with compact global context aggregation, improving detail fidelity while preserving semantic consistency across ultra-high-resolution frames.

• High-Frequency-Awareness Training Objective: ✨ We propose HFATO, a training objective that explicitly enhances the model’s ability to recover high-frequency textures and structural details from degraded latent inputs, leading to sharper and more realistic video generation results.

Figure: Overview of the ViBe framework. 🧩

Results 🏆

ViBe outperforms previous state-of-the-art methods on VBench, with significant improvements in aesthetic appeal, imaging quality, and overall consistency. 🌈

Citation 📚

If you use ViBe in your research, please cite our paper:

@misc{wu2026vibeultrahighresolutionvideosynthesis,
      title={ViBe: Ultra-High-Resolution Video Synthesis Born from Pure Images}, 
      author={Yunfeng Wu and Hongying Cheng and Zihao He and Songhua Liu},
      year={2026},
      eprint={2603.23326},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.23326}, 
}