מושגי ליבה
Translation-based video-to-video synthesis aims to transform videos between distinct domains while preserving temporal continuity and underlying content features, enabling applications such as video super-resolution, colorization, and segmentation.
תקציר
This comprehensive review examines the latest progress in the field of translation-based video-to-video synthesis (TVS). It thoroughly investigates emerging methodologies, shedding light on the fundamental concepts and mechanisms utilized for proficient video synthesis.
The review first categorizes TVS approaches into two broad groups based on the input data type: image-to-video (i2v) translation and video-to-video (v2v) translation. It then further divides v2v translation into paired and unpaired methods.
Paired v2v methods require one-to-one mapping between input and output video frames, while unpaired v2v methods aim to determine the mapping between source and target domains without knowing the frame-level correspondence. Unpaired v2v has gained significant attention due to the challenges in obtaining paired datasets.
The review examines various unpaired v2v approaches, including 3D GAN-based methods, temporal constraint-based techniques, optical flow-based algorithms, RNN-based models, and extended i2i translation-based frameworks. It discusses the strengths, limitations, and potential applications of these methods.
The survey also covers evaluation metrics used to assess the performance of TVS models, categorizing them into statistical similarity, semantic consistency, and motion consistency measures. These metrics provide quantitative insights into the quality, realism, and temporal coherence of the synthesized videos.
Finally, the review highlights future research directions and open challenges in the field of translation-based video-video synthesis, such as improving long-term temporal consistency, handling complex scene dynamics, and enhancing generalization capabilities.
סטטיסטיקה
The content does not contain any specific numerical data or metrics. It focuses on providing a comprehensive overview of the field of translation-based video-to-video synthesis, discussing the various approaches and their characteristics.
ציטוטים
"Translation-based Video Synthesis (TVS) has emerged as a vital research area in computer vision, aiming to facilitate the transformation of videos between distinct domains while preserving both temporal continuity and underlying content features."
"One of the principal challenges faced in TVS is the inherent risk of introducing flickering artifacts and inconsistencies between frames during the synthesis process. This is particularly challenging due to the necessity of ensuring smooth and coherent transitions between video frames."
"Unpaired v2v methods aim to bypass the strict requirement of frame-by-frame annotation by devising strategies that utilize unpaired data more efficiently. These methods embrace the inherent relationships between different video domains and leverage this intrinsic information to enable translation without explicit one-to-one correspondence between input and output frames."