TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models
📌 核心贡献
TUNA提出了一个原生统一多模态模型（UMM），通过级联VAE编码器和表示编码器来构建统一的连续视觉表示。该方法旨在克服现有UMM中由于独立编码器导致的表示格式不匹配问题，通过统一的视觉空间实现对图像和视频的端到端处理，从而在理解和生成任务中超越解耦的替代方案。
📖 摘要
Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA’s unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.
🔍 关键信息
| 字段 | 内容 |
|---|---|
| 发表 | 2025-12-01 |
| 分类 | cs.CV |
| 链接 | arXiv |