We present Vivid-VR, a DiT-based generative video restoration method built upon an advanced T2V foundation model, where ControlNet is leveraged to control the generation process, ensuring content consistency. However, conventional fine-tuning of such controllable pipelines frequently suffers from distribution drift due to limitations in imperfect multimodal alignment, resulting in compromised texture realism and temporal coherence. To tackle this challenge, we propose a concept distillation training strategy that utilizes the pretrained T2V model to synthesize training samples with embedded textual concepts, thereby distilling its conceptual understanding to preserve texture and temporal quality. To enhance generation controllability, we redesign the control architecture with two key components: 1) a control feature projector that filters degradation artifacts from input video latents to minimize their propagation through the generation pipeline, and 2) a new ControlNet connector employing a dual-branch design. This connector synergistically combines MLP-based feature mapping with cross-attention mechanism for dynamic control feature retrieval, enabling both content preservation and adaptive control signal modulation. Extensive experiments show that Vivid-VR performs favorably against existing approaches on both synthetic and real-world benchmarks, as well as AIGC videos, achieving impressive texture realism, visual vividness, and temporal consistency. The codes and checkpoints are publicly available at https://github.com/csbhr/Vivid-VR.

翻译：本文提出Vivid-VR，一种基于先进文本到视频基础模型的DiT生成式视频修复方法，该方法利用ControlNet控制生成过程以确保内容一致性。然而，此类可控流程的传统微调常因不完善的多模态对齐限制而遭受分布漂移，导致纹理真实性与时序连贯性受损。为应对此挑战，我们提出一种概念蒸馏训练策略，利用预训练的文本到视频模型合成嵌入文本概念的训练样本，从而蒸馏其概念理解以保持纹理与时序质量。为增强生成可控性，我们重新设计了控制架构，包含两个核心组件：1）控制特征投影器，从输入视频隐变量中滤除退化伪影，以最小化其通过生成管道的传播；2）采用双分支设计的新型ControlNet连接器。该连接器协同结合基于MLP的特征映射与交叉注意力机制，实现动态控制特征检索，从而同时支持内容保持与自适应控制信号调制。大量实验表明，Vivid-VR在合成与真实世界基准测试以及AIGC视频上均优于现有方法，实现了卓越的纹理真实感、视觉生动性与时序一致性。代码与模型检查点已公开于https://github.com/csbhr/Vivid-VR。