Multimodal Machine Translation (MMT) typically enhances text-only translation by incorporating aligned visual features. Despite the remarkable progress, state-of-the-art MMT approaches often rely on paired image-text inputs at inference and are sensitive to irrelevant visual noise, which limits their robustness and practical applicability. To address these issues, we propose D2P-MMT, a diffusion-based dual-branch prompting framework for robust vision-guided translation. Specifically, D2P-MMT requires only the source text and a reconstructed image generated by a pre-trained diffusion model, which naturally filters out distracting visual details while preserving semantic cues. During training, the model jointly learns from both authentic and reconstructed images using a dual-branch prompting strategy, encouraging rich cross-modal interactions. To bridge the modality gap and mitigate training-inference discrepancies, we introduce a distributional alignment loss that enforces consistency between the output distributions of the two branches. Extensive experiments on the Multi30K dataset demonstrate that D2P-MMT achieves superior translation performance compared to existing state-of-the-art approaches. Our code is publicly available at https://github.com/MentaY/DDP.

翻译：多模态机器翻译（MMT）通常通过引入对齐的视觉特征来增强纯文本翻译。尽管取得了显著进展，现有最先进的MMT方法在推理时往往依赖配对的图像-文本输入，且对无关视觉噪声敏感，这限制了其鲁棒性和实际应用性。为解决这些问题，我们提出D2P-MMT——一种基于扩散模型的鲁棒视觉引导翻译双支路提示框架。具体而言，D2P-MMT仅需源文本和由预训练扩散模型生成的图像重建结果，该过程自然过滤掉干扰性视觉细节，同时保留语义线索。在训练阶段，模型通过双支路提示策略联合学习真实图像与重建图像，鼓励丰富的跨模态交互。为弥合模态差异并缓解训练-推理不一致性，我们引入分布对齐损失，强制两个支路的输出分布保持一致。在Multi30K数据集上的大量实验表明，D2P-MMT相比现有最先进方法取得了更优的翻译性能。我们的代码已开源：https://github.com/MentaY/DDP。