Current gait recognition research predominantly focuses on extracting appearance features effectively, but the performance is severely compromised by the vulnerability of silhouettes under unconstrained scenes. Consequently, numerous studies have explored how to harness information from various models, particularly by sufficiently utilizing the intrinsic information of skeleton sequences. While these model-based methods have achieved significant performance, there is still a huge gap compared to appearance-based methods, which implies the potential value of bridging silhouettes and skeletons. In this work, we make the first attempt to reconstruct dense body shapes from discrete skeleton distributions via the diffusion model, demonstrating a new approach that connects cross-modal features rather than focusing solely on intrinsic features to improve model-based methods. To realize this idea, we propose a novel gait diffusion model named DiffGait, which has been designed with four specific adaptations suitable for gait recognition. Furthermore, to effectively utilize the reconstructed silhouettes and skeletons, we introduce Perception Gait Integration (PGI) to integrate different gait features through a two-stage process. Incorporating those modifications leads to an efficient model-based gait recognition framework called ZipGait. Through extensive experiments on four public benchmarks, ZipGait demonstrates superior performance, outperforming the state-of-the-art methods by a large margin under both cross-domain and intra-domain settings, while achieving significant plug-and-play performance improvements.

翻译：当前步态识别研究主要集中于有效提取外观特征，但在无约束场景下轮廓的脆弱性严重制约了其性能。因此，众多研究探索如何利用多种模型的信息，特别是通过充分挖掘骨架序列的内在信息。尽管这些基于模型的方法已取得显著性能，但与基于外观的方法相比仍存在巨大差距，这暗示了连接轮廓与骨架的潜在价值。本研究首次尝试通过扩散模型从离散骨架分布重建密集人体形状，展示了一种通过连接跨模态特征而非仅关注内在特征来改进基于模型方法的新途径。为实现这一构想，我们提出了一种名为DiffGait的新型步态扩散模型，该模型针对步态识别任务设计了四项适应性改进。此外，为有效利用重建的轮廓与骨架，我们引入感知步态集成（PGI）机制，通过两阶段流程融合不同步态特征。整合这些改进后，我们构建了名为ZipGait的高效基于模型的步态识别框架。通过在四个公开基准上的大量实验，ZipGait展现出卓越性能，在跨域与域内设置下均以显著优势超越现有最优方法，同时实现了显著的即插即用性能提升。