Show HN: DeepDream for Video with Temporal Consistency (github.com)

A new PyTorch fork of DeepDream, titled "DeepDream for Video with Temporal Consistency," has been released, enhancing the original single-image application of DeepDream to support video processing while ensuring temporal consistency. This implementation employs RAFT Optical Flow for frame warping, enabling smoother transitions and better object tracking across frames. Additionally, it features occlusion masking capabilities to automatically address potential ghosting artifacts that occur when objects move in front of others, thus improving the visual coherence of the resulting videos. This development is significant for the AI and machine learning community as it merges concepts of computer vision and neural style transfer, allowing artists and developers to create seamless, dream-like video effects. The setup leverages popular libraries like OpenCV and NumPy, and the process requires fewer iterations per frame, thanks to the retained visual continuity from previous frames. Users can easily execute video transformations with specific commands, showcasing the project's accessibility for both researchers and hobbyists interested in generative art.