Multi-Modal Latent Diffusion for Audio-Visual Speech Synthesis
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Solution Overview
Problem
Existing technologies for synthesizing talking head videos focus on separate operations for audio and video, leading to suboptimal results, while joint synthesis of audio and video has not been adequately explored.
Innovation Solution
A method using multi-modal latent diffusion models to jointly synthesize audio-visual speech, incorporating text-conditioned generation and shared information between modalities through a denoising neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate operations are used for audio and video synthesis, then device complexity is reduced, but synchronization quality and overall synthesis quality deteriorate
Solution Approach 1:
The patent merges separate audio and video synthesis operations into a unified joint synthesis system. The model simultaneously processes audio inputs and generates corresponding video outputs, ensuring temporal and semantic synchronization between the two modalities. This is achieved through a unified neural network architecture that learns the joint distribution of audio-visual data, resolving the contradiction by prioritizing synchronization quality while managing complexity through integrated design.
2Manufacturing precision
If joint synthesis of audio and video is implemented, then synchronization quality and synthesis quality improve, but device complexity increases
Solution Approach 1:
The patent segments the joint synthesis system into distinct functional modules: an audio encoder that processes audio inputs, a video decoder that generates video outputs, and a joint training mechanism that coordinates both. This modular segmentation allows the system to achieve high synthesis quality through coordinated operation while managing complexity by organizing functions into separate, manageable components with defined interfaces.
Solution Approach 2:
The patent implements a universal model that handles multiple functions within a single system: audio processing, video generation, and synchronization. The unified architecture learns joint audio-visual representations that enable the system to perform both audio encoding and video decoding tasks, reducing overall system complexity while maintaining high synthesis quality through shared computational resources and coordinated optimization.
3Ease of operation
If audio-driven talking head generation is used, then ease of operation is improved, but synthesis quality deteriorates due to lack of multi-modal coordination
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically coordinates audio and video generation without requiring separate manual operations. The joint synthesis model inherently aligns audio-visual temporal and semantic relationships through its training objective, enabling the system to self-optimize synchronization and quality. This maintains ease of operation with simple audio input while achieving high synthesis quality through automated multi-modal coordination.
Data Source
AI summary
Methods, systems, and computer programs are presented for audio-visual speech generation with multi-modal latent diffusion models. One method includes encoding raw audio signals and video frames into respective latent spaces using audio and visual autoencoders. A text transcript is processed into phoneme sequences using a text transcript processor. The audio and visual latent spaces are conditioned using the text transcript and a conditioning variable. Joint distributions of the visual and audio latent spaces, text transcript, and conditioning variable are learned using a multi-modal latent diffusion model. The model adds noise to the latent audio-visual representations and predicts the noise through denoising neural networks. An inverted diffusion process is utilized to generate diverse speech content and speaker characteristics, resulting in realistic audio-visual speech. The technology presented provides a novel approach to conditional speech generation with potential applications in speech synthesis, voice conversion, and speech recognition.