Tunnel Try-on is a novel diffusion-based framework for video virtual try-on that addresses the challenges of preserving clothing details and generating coherent motion in videos. The method introduces a "focus tunnel" strategy to zoom in on clothing regions, enabling better detail preservation. It uses Kalman filtering to smooth the tunnel and integrates position embeddings into temporal attention layers to enhance motion consistency. An environment encoder is also developed to extract global context for background generation. The model outperforms existing methods in both qualitative and quantitative evaluations, achieving state-of-the-art performance on the VVT dataset and other benchmarks. The framework is effective in handling complex scenarios with diverse clothing types and dynamic human movements, providing high-fidelity video try-on results. The approach combines diffusion models with video generation techniques, offering a practical solution for the fashion industry. The model's key innovations include the focus tunnel extraction, tunnel enhancement, and environment encoding, which together improve temporal consistency and background generation. The results demonstrate that Tunnel Try-on is a significant advancement in video virtual try-on technology.Tunnel Try-on is a novel diffusion-based framework for video virtual try-on that addresses the challenges of preserving clothing details and generating coherent motion in videos. The method introduces a "focus tunnel" strategy to zoom in on clothing regions, enabling better detail preservation. It uses Kalman filtering to smooth the tunnel and integrates position embeddings into temporal attention layers to enhance motion consistency. An environment encoder is also developed to extract global context for background generation. The model outperforms existing methods in both qualitative and quantitative evaluations, achieving state-of-the-art performance on the VVT dataset and other benchmarks. The framework is effective in handling complex scenarios with diverse clothing types and dynamic human movements, providing high-fidelity video try-on results. The approach combines diffusion models with video generation techniques, offering a practical solution for the fashion industry. The model's key innovations include the focus tunnel extraction, tunnel enhancement, and environment encoding, which together improve temporal consistency and background generation. The results demonstrate that Tunnel Try-on is a significant advancement in video virtual try-on technology.