TempoSyncDiff

Distilled Temporally-Consistent Diffusion for Low-Latency Audio-Driven Talking Head Generation

Welcome, esteemed visitor.

This repository contains the official implementation of TempoSyncDiff, a framework for low-latency audio-driven talking-head generation using distilled temporally-consistent diffusion models. Its central ambition is to produce visually coherent talking-head videos while persuading diffusion models to accomplish more with fewer steps and, ideally, less dramatic contemplation.

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📄 Paper

Title:
TempoSyncDiff: Distilled Temporally-Consistent Diffusion for Low-Latency Audio-Driven Talking Head Generation

arXiv:
https://arxiv.org/abs/

This work is currently submitted for peer review.
If the paper is published, this repository will be updated accordingly.

Until then, the arXiv manuscript shall serve as the principal public reference, with all due scholarly dignity.

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🧠 Overview

Diffusion models are capable of remarkable visual synthesis, although they are occasionally fond of taking their time. TempoSyncDiff explores whether a teacher–student distillation strategy can preserve much of the denoising quality of a stronger diffusion model while enabling few-step inference for more practical deployment.

The framework incorporates:

• Identity anchoring, to help the generated subject remain recognizably the intended person
• Temporal regularization, to reduce frame-to-frame flicker
• Viseme-based conditioning, to provide coarse lip-motion control from speech

In summary, the method seeks to generate stable talking-head videos with lower latency, which is useful for researchers, developers, and anyone whose GPU has learned the meaning of restraint.

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⚙️ Method Highlights

The proposed framework investigates:

• Latent diffusion
• Teacher–student distillation
• Few-step denoising
• Temporal consistency objectives
• Identity preservation mechanisms
• Audio-conditioned viseme control

The overall design attempts to balance:

1. Visual realism
2. Temporal stability
3. Efficient inference

Securing all three at once remains a noble scientific negotiation.

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🗂 Repository Structure

TempoSyncDiff/
│
├── models/                # Model architectures
├── training/              # Teacher and student training scripts
├── inference/             # Few-step inference pipeline
├── evaluation/            # Metrics and evaluation utilities
├── configs/               # Configuration files
└── docs/                  # Figures and additional documentation

The repository will continue to expand as the project matures and the codebase develops further composure.

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📊 Datasets

Experiments primarily involve:

• LRS3-TED
• HDTF

Please obtain the datasets from their official sources and comply with the corresponding licenses and usage terms.

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💻 Hardware Notes

The framework is designed to explore low-latency inference, including:

• CPU-only execution
• Edge-oriented feasibility
• Reduced-step latent diffusion

Preliminary experiments suggest that few-step diffusion may be practical under constrained settings, provided one approaches computational optimism with suitable moderation.

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🚧 Repository Status

This repository is under active development.

Certain components may presently be:

• experimental
• under refinement
• awaiting additional documentation
• behaving with the confidence of early research code

Updates will be provided as the project progresses.

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📜 Citation

If this work contributes to your research, please consider citing:

@article{mazumdar2026temposyncdiff,
  title={TempoSyncDiff: Distilled Temporally-Consistent Diffusion for Low-Latency Audio-Driven Talking Head Generation},
  author={Mazumdar, Soumya and Rakesh, Vineet Kumar},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2026}
}

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🤝 Acknowledgements

This work benefited from research support, infrastructure, and institutional assistance from:

• Variable Energy Cyclotron Centre (VECC)
• Department of Atomic Energy (DAE), Government of India

The authors also acknowledge the broader research community, whose collective efforts continue to make advanced generative models both possible and delightfully ambitious.

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⚠️ Responsible Use

Talking-head generation systems should be used responsibly.

Users are encouraged to ensure appropriate consent, respect dataset and content usage conditions, and clearly indicate when generated media is synthetic.

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📬 Contact

Soumya Mazumdar
reachme@soumyamazumdar.com

Thank you for visiting this repository. May your experiments converge, your logs remain readable, and your diffusion steps be few yet effective.