# Uni-ViGU：基于扩散式视频生成器统一视频生成与理解

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-09 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnygovrl003csl1332ixal16
- 原文链接：https://arxiv.org/abs/2604.08121

## AI 摘要

Uni-ViGU框架通过扩展扩散式视频生成器统一视频生成与理解，反转了传统以理解为中心的多模态模型范式。该框架采用统一流方法，在单一过程中对视频进行连续流匹配、对文本进行离散流匹配；引入模态驱动的MoE架构，以轻量级层增强Transformer实现文本生成；并通过双向训练机制（知识召回与能力细化两阶段）将生成知识迁移至理解任务。实验表明，该模型在视频生成与理解任务上均达到竞争性性能，验证了以生成为中心的架构路径可行性。

## 正文

Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher computational costs than understanding, particularly for video. This imbalance motivates us to invert the conventional paradigm: rather than extending understanding-centric MLLMs to support generation, we propose Uni-ViGU, a framework that unifies video generation and understanding by extending a video generator as the foundation. We introduce a unified flow method that performs continuous flow matching for video and discrete flow matching for text within a single process, enabling coherent multimodal generation. We further propose a modality-driven MoE-based framework that augments Transformer blocks with lightweight layers for text generation while preserving generative priors. To repurpose generation knowledge for understanding, we design a bidirectional training mechanism with two stages: Knowledge Recall reconstructs input prompts to leverage learned text-video correspondences, while Capability Refinement fine-tunes on detailed captions to establish discriminative shared representations. Experiments demonstrate that Uni-ViGU achieves competitive performance on both video generation and understanding, validating generation-centric architectures as a scalable path toward unified multimodal intelligence. Project Page and Code: https://fr0zencrane.github.io/uni-vigu-page/.