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MoDiTalker: A Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation

Core Concepts
MoDiTalker, a novel motion-disentangled diffusion model, generates high-fidelity talking head videos by explicitly separating the generation process into audio-to-motion and motion-to-video stages.
The paper introduces MoDiTalker, a novel framework for high-fidelity talking head generation. It consists of two distinct diffusion models: Audio-to-Motion (AToM): AToM is a transformer-based diffusion model that generates facial landmark sequences synchronized with the input audio. It leverages an audio attention mechanism to capture subtle lip movements and disentangles the processing of lip-related and lip-unrelated facial regions to enhance lip synchronization. Motion-to-Video (MToV): MToV is an efficient video diffusion model that generates high-quality talking head videos conditioned on the facial landmark sequences from AToM, identity frames, and pose frames. It utilizes tri-plane representations to effectively condition the video diffusion model, improving temporal consistency and reducing computational complexity compared to previous diffusion-based methods. The experiments demonstrate that MoDiTalker outperforms state-of-the-art GAN-based and diffusion-based approaches on standard benchmarks in terms of video quality, lip synchronization, and identity preservation. Comprehensive ablation studies validate the effectiveness of the proposed components.
"We leverage attention mechanisms to distinguish between lip-related and lip-unrelated regions." "Our model needs only 23 seconds to produce a 5-second video at 25 fps on a single Nvidia 24GB 3090 RTX GPU, which is 43 times faster than DiffTalk and 31 times faster than Diffused Heads."
"To overcome these challenges, we propose a novel motion-disentangled diffusion model for high-quality talking head generation, dubbed MoDiTalker." "Combining these, MoDiTalker enables high-fidelity talking head generation with enhanced temporal consistency and substantially reduced inference time complexity compared to existing works."

Key Insights Distilled From

by Seyeon Kim,S... at 03-29-2024

Deeper Inquiries

How can the proposed motion-disentangled approach be extended to other video generation tasks beyond talking head synthesis?

The motion-disentangled approach proposed in MoDiTalker can be extended to other video generation tasks by adapting the framework to different types of visual content. For instance, the concept of disentangling motion from the visual content can be applied to tasks such as gesture generation, action recognition, or even full-body motion synthesis. By separating the motion information from the visual data, the model can learn to generate realistic and coherent movements in various contexts. Additionally, the use of tri-plane representations and efficient conditioning mechanisms can be tailored to different types of video data, enabling the generation of diverse and high-quality visual content beyond talking head synthesis.

What are the potential limitations of the current MoDiTalker framework, and how could it be further improved to handle more diverse audio-visual scenarios?

One potential limitation of the current MoDiTalker framework could be its performance in handling extremely complex audio-visual scenarios or scenarios with multiple speakers. The model may struggle to disentangle motion and generate accurate lip movements in highly dynamic or noisy audio inputs. To address this, the framework could be enhanced by incorporating multi-speaker audio processing techniques, such as speaker diarization and separation, to better handle diverse audio inputs. Additionally, the model could benefit from more robust attention mechanisms that can adapt to varying levels of audio complexity and speaker characteristics. Improving the model's ability to generalize across different audio-visual scenarios and adapt to new speakers could further enhance its performance in handling diverse audio-visual tasks.

Given the advancements in text-to-image diffusion models, how could a similar approach be applied to generate talking head videos directly from text descriptions?

To apply a similar approach to generate talking head videos directly from text descriptions, the model could be modified to process text inputs and generate corresponding facial motion sequences. By incorporating a text encoder to convert textual descriptions into latent representations, the model can learn to generate facial movements that align with the content of the text. The diffusion model can then be conditioned on these text embeddings to generate realistic talking head videos that convey the information in the text. Additionally, leveraging techniques from text-to-image generation, such as attention mechanisms and transformer architectures, can help the model capture the nuances of speech and facial expressions from textual input, enabling the generation of expressive and coherent talking head videos directly from text descriptions.