Summary of Ditto: Motion-space Diffusion For Controllable Realtime Talking Head Synthesis, by Tianqi Li et al.
Ditto: Motion-Space Diffusion for Controllable Realtime Talking Head Synthesis
by Tianqi Li, Ruobing Zheng, Minghui Yang, Jingdong Chen, Ming Yang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advances in diffusion models have transformed audio-driven talking head synthesis, enabling precise lip synchronization and natural head movements aligned with the audio signal. However, current methods struggle with slow inference speed, limited facial motion control, and visual artifacts due to Variational Auto-Encoders (VAE) latent spaces. To overcome these limitations, we present Ditto, a diffusion-based framework for controllable real-time talking head synthesis. Ditto bridges motion generation and photorealistic neural rendering through an explicit identity-agnostic motion space, replacing VAE representations and reducing complexity while enabling precise control. We propose an inference strategy optimizing audio feature extraction, motion generation, and video synthesis, achieving streaming processing, real-time inference, and low first-frame delay crucial for interactive applications like AI assistants. Our experimental results demonstrate Ditto’s capabilities in generating compelling talking head videos, outperforming existing methods in both motion control and real-time performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better computer-generated talking heads that can move naturally and synchronize with audio. Right now, these talking heads are limited by slow processing times, lack of control over facial expressions, and some visual problems. The authors created a new method called Ditto to fix these issues. Ditto combines different parts together in a way that makes it faster, more controllable, and better looking. This means we can use it in applications like AI assistants where the talking head needs to be able to respond quickly and naturally. |
Keywords
» Artificial intelligence » Diffusion » Feature extraction » Inference
