To extend the proposed framework to handle videos and achieve temporally consistent colorization, several modifications and additions can be made:
Temporal Consistency Module: Introduce a module that considers the temporal information in videos to ensure consistency in colorization across frames. This module can leverage techniques like optical flow estimation to align colors between consecutive frames.
Frame Interpolation: Implement frame interpolation techniques to generate colorization results for intermediate frames based on the colorization of keyframes. This can help maintain consistency and smooth transitions between frames.
Spatio-Temporal Attention: Incorporate spatio-temporal attention mechanisms to focus on relevant regions across frames and ensure that colorization remains consistent and realistic throughout the video sequence.
Video-specific Loss Functions: Develop loss functions tailored for video colorization tasks, considering factors like motion coherence, temporal smoothness, and frame-to-frame color consistency.
Efficient Inference: Optimize the framework for video processing by considering efficient inference strategies to handle the computational complexity of colorizing multiple frames in a video sequence.
By incorporating these elements, the framework can be extended to handle videos and achieve temporally consistent colorization, providing realistic and visually pleasing colorized video outputs.