Advancing Video Translation with Positional Feature Embeddings: A Biotechnological Integration Approach

Abstract

This study introduces a novel enhancement module for video translation, termed Positional Feature Embedding (PFE), designed to leverage spatiotemporal dynamics within video frames to improve translation quality. The PFE module integrates interval frame motion features into video translation tasks by constructing positional embeddings that capture the frame-to-frame differences, reflecting both spatial positioning and temporal continuity. The innovation of PFE lies in its ability to enhance the model's focus on the locational attributes of moving subjects across sequential frames, thereby improving the continuity and clarity of translated video content. This approach is particularly advantageous for translating videos involving dynamic scenes where accurate representation of motion and change over time is critical. Utilizing Cycle GAN as a benchmark, our experiments demonstrate that integrating the PFE module into the video translation process substantially boosts performance. The results underscore the critical role of motion feature embeddings in enhancing the effectiveness of video translation models. By accurately encoding and integrating motion-related information, the PFE-enhanced translation models offer significant improvements in video quality and fidelity, making them highly suitable for applications in biotechnological contexts where precise video analysis and translation are required. This research not only advances video translation technology but also highlights the potential for applying such innovations in biotechnological fields, where enhanced video translation can support advanced research and development activities.