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：我们提出了Vivid-VR，一种基于DiT的生成式视频修复方法，该方法建立在先进的T2V基础模型之上，并利用ControlNet来控制生成过程以确保内容一致性。然而，由于不完善的多模态对齐存在局限，此类可控管道的常规微调常因分布漂移而受损，导致纹理真实感和时序连贯性下降。为应对这一挑战，我们提出了一种概念蒸馏训练策略，该策略利用预训练的T2V模型合成嵌入文本概念的训练样本，从而提炼其概念理解以保持纹理和时序质量。为增强生成可控性，我们重新设计了控制架构，包含两个关键组件：1）一个控制特征投影器，用于从输入视频潜变量中滤除退化伪影，以最小化其通过生成管道的传播；2）一种采用双分支设计的新型ControlNet连接器。该连接器协同结合了基于MLP的特征映射与交叉注意力机制，用于动态控制特征检索，从而实现内容保持和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，实现了令人印象深刻的纹理真实感、视觉生动性和时序一致性。代码和检查点已在https://github.com/csbhr/Vivid-VR公开提供。