Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling
🤖 AI Summary
To address the challenge of simultaneously achieving bandwidth efficiency and dynamic fidelity in real-time video motion transmission, this paper proposes a lightweight motion transfer framework based on generative time-series modeling. We innovatively integrate two generative sequential models—Variational Recurrent Neural Network (VRNN) and GRU-based Normalizing Flow (GRU-NF)—into the First-Order Motion Model (FOMM), respectively optimizing multi-step prediction accuracy and future-frame diversity. The framework combines self-supervised keypoint detection, optical flow estimation, and generative synthesis to enable high-fidelity motion reconstruction and strong generalization under low frame rates. Evaluated on three benchmark datasets, our approach achieves significant improvements in MAE, SSIM, JPEG compression ratio, and Average Prediction Distance (APD). Specifically, VRNN-FOMM excels in multi-step motion prediction, while GRU-NF-FOMM generates high-quality, diverse future frames, demonstrating superior performance in real-time anomaly detection. The method provides an efficient, low-bandwidth solution for applications including video conferencing, VR interaction, and remote health monitoring.
Application Category
📝 Abstract
We propose a deep learning framework designed to significantly optimize bandwidth for motion-transfer-enabled video applications, including video conferencing, virtual reality interactions, health monitoring systems, and vision-based real-time anomaly detection. To capture complex motion effectively, we utilize the First Order Motion Model (FOMM), which encodes dynamic objects by detecting keypoints and their associated local affine transformations. These keypoints are identified using a self-supervised keypoint detector and arranged into a time series corresponding to the successive frames. Forecasting is performed on these keypoints by integrating two advanced generative time series models into the motion transfer pipeline, namely the Variational Recurrent Neural Network (VRNN) and the Gated Recurrent Unit with Normalizing Flow (GRU-NF). The predicted keypoints are subsequently synthesized into realistic video frames using an optical flow estimator paired with a generator network, thereby facilitating accurate video forecasting and enabling efficient, low-frame-rate video transmission. We validate our results across three datasets for video animation and reconstruction using the following metrics: Mean Absolute Error, Joint Embedding Predictive Architecture Embedding Distance, Structural Similarity Index, and Average Pair-wise Displacement. Our results confirm that by utilizing the superior reconstruction property of the Variational Autoencoder, the VRNN integrated FOMM excels in applications involving multi-step ahead forecasts such as video conferencing. On the other hand, by leveraging the Normalizing Flow architecture for exact likelihood estimation, and enabling efficient latent space sampling, the GRU-NF based FOMM exhibits superior capabilities for producing diverse future samples while maintaining high visual quality for tasks like real-time video-based anomaly detection.
Problem
Research questions and friction points this paper is trying to address.
Optimize bandwidth for real-time video motion transfer applications
Capture complex motion using First Order Motion Model (FOMM)
Forecast keypoints with generative time series models for video synthesis
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses First Order Motion Model for motion encoding
Integrates VRNN and GRU-NF for keypoint forecasting
Synthesizes frames with optical flow and generator network
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