  Generative models (GMs) have received increasing research interest for their
remarkable capacity to achieve comprehensive understanding. However, their
potential application in the domain of multi-modal tracking has remained
relatively unexplored. In this context, we seek to uncover the potential of
harnessing generative techniques to address the critical challenge, information
fusion, in multi-modal tracking. In this paper, we delve into two prominent GM
techniques, namely, Conditional Generative Adversarial Networks (CGANs) and
Diffusion Models (DMs). Different from the standard fusion process where the
features from each modality are directly fed into the fusion block, we
condition these multi-modal features with random noise in the GM framework,
effectively transforming the original training samples into harder instances.
This design excels at extracting discriminative clues from the features,
enhancing the ultimate tracking performance. To quantitatively gauge the
effectiveness of our approach, we conduct extensive experiments across two
multi-modal tracking tasks, three baseline methods, and three challenging
benchmarks. The experimental results demonstrate that the proposed
generative-based fusion mechanism achieves state-of-the-art performance,
setting new records on LasHeR and RGBD1K.
