Talking Head Video Synthesis via Segmented Temporal Modeling
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Solution Overview
Problem
Conventional talking head video synthesis methods relying on autoregressive models are complex and time-consuming, especially for high-resolution images, due to their dependency on establishing frame relationships.
Innovation Solution
A method involving an electronic device with a processor and storage that performs feature extraction on speech and observation data, followed by temporal modeling using autoregressive models to obtain low-dimensional representations, which reduces complexity and synthesis time by processing sensitive and insensitive temporal features separately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autoregressive models are used to establish dependencies between video frames, then the synthesis quality is improved, but the complexity and synthesis time increase significantly
Solution Approach 1:
The patent segments the temporal modeling process into two distinct parts: (1) extracting temporal features from speech and observation data using autoregressive models, and (2) generating video frames in parallel using diffusion models. This segmentation allows the autoregressive component to focus only on feature extraction while the frame generation is parallelized, reducing overall complexity and synthesis time while maintaining quality.
2Reliability
If autoregressive models are used to establish dependencies between video frames, then the synthesis quality is improved, but the synthesis time increases significantly
Solution Approach 1:
The patent separates temporal dependency modeling from frame generation. Temporal features are extracted once from speech and observation data, then these features are used to guide parallel frame generation. This segmentation eliminates the need to process each frame sequentially through autoregressive models, dramatically reducing synthesis time while preserving temporal consistency through the extracted features.
Solution Approach 2:
The patent performs preliminary temporal feature extraction from speech and observation data before frame generation. By pre-processing the temporal dependencies and encoding them into latent features, the system prepares all necessary temporal information in advance, allowing subsequent frame generation to proceed in parallel without iterative dependencies, thus reducing synthesis time.
3Manufacturing precision
If high-resolution images are used for synthesis, then the output quality is improved, but the synthesis time increases due to frame dependency processing
Solution Approach 1:
The patent segments the synthesis process into low-dimensional temporal feature extraction and high-dimensional parallel frame generation. This allows high-resolution image synthesis to be performed in parallel for multiple frames simultaneously, rather than sequentially processing each high-resolution frame through autoregressive models, thus maintaining image quality while reducing synthesis time.
Data Source
AI summary
A method for synthesizing a talking head video includes: obtaining speech data to be synthesized and observation data, wherein the observation data is data obtained through observation other than the speech data; performing feature extraction on the speech data to obtain speech features corresponding to the speech data, and performing feature extraction on the observation data to obtain non-speech features corresponding to the observation data; performing temporal modeling on the speech features and first non-speech features to obtain low-dimensional representations, wherein the first non-speech features are non-speech features that are sensitive to temporal changes; and performing video synthesis based on the low-dimensional representations and second non-speech features, wherein the second non-speech features are non-speech features insensitive to temporal changes.