# FluentAvatar: Flicker-Free Talking-Head Animation via Phoneme-Guided Autoregressive Modeling

arXiv: https://arxiv.org/abs/2509.12052 · HTML: https://mlsanity.com/paper/2509.12052/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 53 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 30 of 31)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://mlsanity.com/paper/2404.02905/) (2024)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation](https://mlsanity.com/paper/2406.06525/) (2024)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion](https://mlsanity.com/paper/2506.08009/) (2025)
- [VideoPoet: A Large Language Model for Zero-Shot Video Generation](https://mlsanity.com/paper/2312.14125/) (2023)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation](https://mlsanity.com/paper/2104.11116/) (2021)
- [Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss](https://mlsanity.com/paper/1905.03820/) (2019)
- [Talking Face Generation by Adversarially Disentangled Audio-Visual Representation](https://mlsanity.com/paper/1807.07860/) (2018)
- [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://mlsanity.com/paper/2407.03168/) (2024)
- [AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation](https://mlsanity.com/paper/2403.17694/) (2024)
- [VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time](https://mlsanity.com/paper/2404.10667/) (2024)
- [Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation](https://mlsanity.com/paper/2409.04410/) (2024)
- [OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models](https://mlsanity.com/paper/2502.01061/) (2025)
- [Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency](https://mlsanity.com/paper/2409.02634/) (2024)
- [Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation](https://mlsanity.com/paper/2410.07718/) (2024)
- [OmniAvatar: Efficient Audio-Driven Avatar Video Generation with Adaptive Body Animation](https://mlsanity.com/paper/2506.18866/) (2025)
- [StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation](https://mlsanity.com/paper/2508.08248/) (2025)
- [Ditto: Motion-Space Diffusion for Controllable Realtime Talking Head Synthesis](https://mlsanity.com/paper/2411.19509/) (2024)
- [Learned Spatial Representations for Few-shot Talking-Head Synthesis](https://mlsanity.com/paper/2104.14557/) (2021)
- [ARTalk: Speech-Driven 3D Head Animation via Autoregressive Model](https://mlsanity.com/paper/2502.20323/) (2025)
- [A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading](https://mlsanity.com/paper/1908.04917/) (2019)
- [Teller: Real-Time Streaming Audio-Driven Portrait Animation with Autoregressive Motion Generation](https://mlsanity.com/paper/2503.18429/) (2025)

## Built on by (most cited first, 1 of 1)

- [AsymTalker: Identity-Consistent Long-Term Talking Head Generation via Asymmetric Distillation](https://mlsanity.com/paper/2605.02948/) (2026)

## Abstract (arXiv)

Current talking-head generation has gradually shifted from GAN-based methods to diffusion-based paradigms, achieving remarkable progress in visual fidelity and temporal consistency. However, inter-frame flicker remains prevalent in existing diffusion-based methods. An important reason is that denoising trajectory variation induced by stochastic initialization leaves residual inter-frame inconsistencies, which manifest as short-term, abrupt visual fluctuations between adjacent frames. To further verify this, we conduct a controlled study by fixing the input while varying only the random seed. The results show markedly different flicker patterns across samplings, with a mean inter-seed Pearson correlation of only r = 0.15. This motivates us to explore autoregressive generation, which models frames sequentially and provides a more direct prior for temporal continuity. Based on this, we propose FluentAvatar, a two-stage autoregressive framework built on phoneme representations. First, Facial Keyframe Generation produces phoneme-aligned keyframes under a Phoneme-Frame Causal Attention Mask, and Inter-frame Interpolation synthesizes transition frames via a timestamp-aware adaptive strategy built upon selective state space modeling. Moreover, we introduce BG-Flicker, a background-isolated metric for talking-head videos that enables more reliable evaluation of inter-frame flicker. Experiments on CMLR and HDTF demonstrate that FluentAvatar achieves strong performance in visual fidelity, lip synchronization, and temporal stability, attaining the best FVD on both datasets and BG-Flicker results close to ground truth. The code, the model, and the interface will be released to facilitate further research.