STB-VMM: Swin Transformer Based Video Motion Magnification
This work addresses the challenge of magnifying subtle motions in videos for applications like biomedical analysis and deepfake detection, representing an incremental improvement over existing techniques.
The paper tackled the problem of noisy and blurry outputs in video motion magnification by proposing a Swin Transformer-based model, achieving state-of-the-art results with less noise, blurriness, and artifacts than prior methods.
The goal of video motion magnification techniques is to magnify small motions in a video to reveal previously invisible or unseen movement. Its uses extend from bio-medical applications and deepfake detection to structural modal analysis and predictive maintenance. However, discerning small motion from noise is a complex task, especially when attempting to magnify very subtle, often sub-pixel movement. As a result, motion magnification techniques generally suffer from noisy and blurry outputs. This work presents a new state-of-the-art model based on the Swin Transformer, which offers better tolerance to noisy inputs as well as higher-quality outputs that exhibit less noise, blurriness, and artifacts than prior-art. Improvements in output image quality will enable more precise measurements for any application reliant on magnified video sequences, and may enable further development of video motion magnification techniques in new technical fields.