HighSync: High-Quality Lip Synchronization via Latent Diffusion Models

arXiv:2605.16918 · cs.CV · Submitted 2026-05-16 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "HighSync: High-Quality Lip Synchronization via Latent Diffusion Models".

Tom: HighSync presents an end-to-end diffusion-based framework for high-fidelity lip synchronization that generates photorealistic talking-face videos aligned with arbitrary input audio, addressing prior limitations in both image quality and temporal consistency.

Jane: First, who's behind it and why it matters.

Title and authors: Tom: So, let's start by looking at the title and who put this paper out there. HighSync: High-Quality Lip Synchronization via Latent Diffusion Models really tells you exactly what the paper is about—it's about using diffusion models to achieve better lip sync quality.

Jane: And the authors, Daghigh, Iranpour Mobarekeh, Alavi, and Bagheri, seem like a solid team tackling this problem head-on with their approach. They are clearly focused on solving that specific issue of reconciling visual quality with synchronization accuracy in one system.

Lu: What's interesting about the title is the mention of "Latent Diffusion Models," which points to leveraging the latent space where these models operate, suggesting efficiency in generating high-fidelity results for video sequences.

Meng: I wonder if focusing on latent diffusion helps manage the computational load compared to training a full high-resolution model from scratch, Lu? It sounds like they're working within an existing framework.

Lalam: From my perspective, the authors are aiming to create something that doesn't just look like a video but actually moves in perfect time with the spoken words because of this diffusion backbone.

The paper's summary: Tom: Now, let’s look at what HighSync actually does according to their summary. Essentially, they present an end-to-end diffusion framework that generates talking-face videos that match any input audio you give it, which is a significant claim.

Jane: What I find particularly compelling in the summary is how they tackle the existing problems by identifying and systematically eliminating a specific data leakage phenomenon that has been undermining temporal modeling in prior work.

Lu: That systematic elimination of leakage sounds like their main contribution, because that’s where they say they were able to successfully integrate a motion module into their training pipeline without needing SyncNet as a supervisor.

Meng: So, the paper is claiming they've achieved temporally coherent lip movements across batches of twelve consecutive frames using this new method instead of relying on external supervision. That’s a big statement about the model's internal learning capacity.

Lalam: It means the model learns the relationship between audio and lip movement directly, which is crucial because it prevents that visual information from just being copied from other parts of the video context.

The paper's improvements: Tom: Let's talk about how they actually improved things. They pinpoint two major sources of data leakage and fixed them: first, face height variation caused by per-frame preprocessing, which they solved by using a maximum face bounding box height across all frames consistently.

Jane: And the second improvement is addressing the biomechanical coupling between upper and lower facial muscles; they did this by introducing a spatially masked attention mechanism in the motion module so lower-face tokens can't attend to upper-face tokens across time.

Lu: That spatial masking sounds like a very clever way to sever that pathway through which the dynamics of the upper face could influence how the lip state is reconstructed, which is exactly what they were worried about.

Meng: From an engineering standpoint, fixing those specific leakage points shows they spent significant time debugging the temporal dependency issues rather than just stacking more layers on top of a standard architecture.

Lalam: And then there's the third fix mentioned, which was enforcing lossless PNG serialization for all mask images to mitigate artifacts from JPEG compression during training. It shows they were meticulous about every detail, not just the main generation process.

Conclusion: Tom: So, wrapping up on the HighSync paper, we've seen that by using this end-to-end latent diffusion framework and tackling those specific leakage issues—the face height variation and the upper-lower muscle coupling—they’ve managed to get state-of-the-art performance at five hundred twelve by five hundred twelve resolution.

Jane: They achieved this through a two-stage training procedure, decoupling visual quality from motion learning, which allowed them to train the temporal module on sequences of twelve frames without needing SyncNet supervision. This is a very solid way to build the temporal component.

Lu: The results they reported are quite strong, showing superior synchronization accuracy on metrics like LSE-C and achieving a silence test score of zero point nine three, which validates their genuine audio conditioning capabilities when compared against baselines.

Meng: I'm interested in the human evaluation scores they mentioned, specifically those mean scores of four point two eight for image quality and four point zero one for synchronization quality; those numbers tell us how well it balances the fidelity with the timing aspect in a real-world sense.

Lalam: Ultimately, HighSync shows that combining latent diffusion modeling with careful data leakage remediation is a powerful way to get truly audio-driven lip sync results without needing external supervisors like SyncNet.

Tom: It really sounds like this paper is laying down a very robust foundation for how we build future systems that can create high-fidelity, controllable talking faces. What an exciting direction for the field.

Saeed Firouzi Daghigh, Majid Iranpour Mobarekeh, Mostafa Alavi, Mehdi Bagheri

Payam Noor University

cs.CV

Submitted: 2026-05-16

Updated: 2026-09-28

Code: https://github.com/saeed5959/high

Importance score: 84/100

The gist: HighSync presents an end-to-end diffusion-based framework for high-fidelity lip synchronization that generates photorealistic talking-face videos aligned with arbitrary input audio, addressing prior

Key concepts

Latent Diffusion Model (LDM)
This is the core generation technique used by HighSync. It works by iteratively refining random noise into a coherent image or video sequence. Instead of working with raw pixels, it operates in a compressed 'latent' space, making the process faster and more efficient for generating high-quality visual content like talking faces.
Reference U-Net
This component processes the input reference image to extract detailed facial identity features. It injects these identity details into every part of the main denoising network. This ensures that even when generating new frames, the resulting lip movements maintain a consistent and accurate visual appearance matching the original person.
Temporal Motion Module
This specialized module is trained to understand how facial expressions change over time across consecutive frames. It learns the dynamics of lip movement specifically, allowing HighSync to generate temporally coherent videos where the mouth movements flow naturally from one frame to the next based on audio input.

Terminology

Summary

HighSync presents an end-to-end diffusion-based framework for high-fidelity lip synchronization that generates photorealistic talking-face videos aligned with arbitrary input audio, addressing prior limitations in both image quality and temporal consistency.

The gist

HighSync is the first end-to-end lip synchronization model to generate temporally coherent, high-fidelity videos at 512×512 resolution without relying on SyncNet supervision.

Model Architecture and Conditioning

The HighSync framework is built upon Stable Diffusion 1.5 (SD 1.5) [10], extended to operate on sequences of 12 consecutive frames through the integration of a temporal motion module [15]. Two conditioning signals drive the generation process: a reference image, which supplies visual identity and facial texture information, and a driving audio signal, which encodes the target speech content to determine lip shape and movement. The Reference U-Net processes the reference image and injects fine-grained identity features into every transformer block of the Denoising U-Net via Reference-Attention layers. Audio features are extracted by Whisper [12] embeddings, which are injected into the Denoising U-Net via Audio-Attention cross-attention layers.

Data Leakage Analysis and Remediation

The core contribution involves identifying and resolving a systematic data leakage problem that undermined temporal audio conditioning in motion-module-based lip sync models. The researchers pinpointed two distinct sources:

  1. Face height variation from per-frame preprocessing: Standard face detection pipelines localize and crop the face region independently in each frame, producing a bounding box that extends from the top of the head to the base of the jaw, causing a relative shift in the vertical position of the upper face (eyes, nose) across frames. This is eliminated by computing the maximum face bounding box height across all frames in a given video and using this fixed height uniformly for all crops.

  2. Biomechanical coupling between upper and lower facial muscles: This leakage is addressed by introducing a spatially masked attention mechanism within the motion module, where lower-face tokens cannot attend to upper-face tokens across the time dimension, thereby severing the pathway through which upper-face dynamics could inform lip state reconstruction. A third source, mask image serialization artifacts from JPEG compression, is mitigated by enforcing lossless PNG serialization for all mask images throughout training.

Training Procedure and Temporal Coherence

The framework employs a two-stage training strategy to decouple visual quality learning from temporal motion learning. Stage 1 trains the full model—excluding the motion module—end-to-end on single-frame data to establish robust visual generation quality, identity preservation via the Reference U-Net, and foundational audio conditioning via Whisper cross-attention. Stage 2 freezes all Stage 1 components and trains only the temporal motion module on sequences of 12 consecutive masked input frames, enabling it to learn rich temporal lip dynamics across batches of 12 consecutive frames, without any reliance on SyncNet as a training supervisor. Temporal coherence for long-form video is maintained using an overlapped diffusion context strategy, where the denoising trajectories of the boundary frames of one group are shared with the initial frames of the next group across all diffusion timesteps, and memory-efficient streaming generation uses cached intermediate states.

Evaluation and Results

HighSync is evaluated across visual quality (FID, CSIM) and synchronization accuracy (LSE-C) on VFHQ, HDTF, and CelebV-HQ datasets. Quantitative results show HighSync achieves state-of-the-art performance: it achieves the best or second-best FID score across all three datasets and surpassing all diffusion-based and GAN-based methods on the LSE-C metric, achieving a silence test score of 0.93, which is substantially higher than baselines, validating its genuine audio conditioning. Human evaluation confirms this performance, with HighSync achieving mean scores of 4.28 for image quality and 4.01 for synchronization quality, demonstrating a superior balance between fidelity and alignment compared to methods like MuseTalk [5]. Ablation studies confirm that combining both leakage remediation strategies yields the best results, reaching an LSE-C of 7.02. Qualitative comparisons show HighSync produces anatomically plausible teeth structures with realistic gum boundaries, outperforming competitors in dental detail and lip texture. The final conclusion is that the combination of latent diffusion modeling, a two-stage training procedure, and data leakage remediation enables genuine audio conditioning under temporal modeling.

Conclusion

HighSync is an end-to-end latent diffusion framework for lip synchronization that simultaneously advances visual generation quality and audio-driven synchronization accuracy by extending Stable Diffusion 1.

Improvements for AI systems

Based on the provided paper, here are the specific improvements that can be made to existing AI systems and what those improved systems will be capable of:

  1. Incorporate a novel, end-to-end latent diffusion framework (HighSync) for high-fidelity lip synchronization at 512x512 resolution.

  2. Develop a two-stage training procedure that decouples visual quality learning (Stage 1) from temporal motion learning (Stage 2).

  3. Implement a dedicated Reference U-Net to condition generation on fine-grained identity and facial texture information, ensuring robust subject preservation across frames.

  4. Integrate audio conditioning using large-scale speech recognition models like Whisper embeddings via cross-attention layers, providing linguistically structured temporal context beyond simple acoustic features.

  5. Systematically eliminate data leakage by normalizing per-frame face bounding box height (to remove mouth aperture variation) and introducing a spatially masked attention mechanism within the motion module (to sever upper-face to lower-face dynamic pathways).

  6. Utilize a motion module incorporating temporal self-attention layers to learn smooth, physically plausible lip trajectories across sequences of 12 consecutive frames, enabling genuine audio dependence.

These improvements will enable the resulting AI system to perform the following specific tasks:

  1. Generate photorealistic talking-face videos at high resolution (512x512) that are visually indistinguishable from authentic footage, free of blurring and dental distortions.

  2. Achieve state-of-the-art lip synchronization accuracy, evidenced by a silence test score approaching 0.93, ensuring the generated lip movement is genuinely driven by the audio signal rather than visual cues or leakage.

  3. Produce highly detailed and anatomically plausible mouth and dental regions, including realistic gum boundaries and individual tooth definitions, which surpasses competitors in fine-grained texture rendering.

  4. Enable robust operation in professional production environments (film/broadcast), where high visual fidelity at high resolution is a prerequisite for tasks like multilingual film dubbing, virtual avatar creation, and advanced video editing.

  5. Allow for the generation of long-form videos through memory-efficient streaming generation techniques, maintaining temporal continuity across multiple generation rounds without prohibitive GPU memory overhead.

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