# ARIG: Autoregressive Interactive Head Generation for Real-time Conversations

arXiv: https://arxiv.org/abs/2507.00472 · HTML: https://mlsanity.com/paper/2507.00472/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 42 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 30 of 36)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [Autoregressive Image Generation without Vector Quantization](https://mlsanity.com/paper/2406.11838/) (2024)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation](https://mlsanity.com/paper/2211.12194/) (2022)
- [EMO: Emote Portrait Alive -- Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions](https://mlsanity.com/paper/2402.17485/) (2024)
- [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://mlsanity.com/paper/2407.03168/) (2024)
- [Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation](https://mlsanity.com/paper/2406.08801/) (2024)
- [EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditions](https://mlsanity.com/paper/2407.08136/) (2024)
- [Autoregressive Video Generation without Vector Quantization](https://mlsanity.com/paper/2412.14169/) (2024)
- [Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion](https://mlsanity.com/paper/2204.08451/) (2022)
- [Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency](https://mlsanity.com/paper/2409.02634/) (2024)
- [From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations](https://mlsanity.com/paper/2401.01885/) (2024)
- [UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes](https://mlsanity.com/paper/2205.10337/) (2022)
- [Responsive Listening Head Generation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2112.13548/) (2021)
- [Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation](https://mlsanity.com/paper/2406.07867/) (2024)
- [INFP: Audio-Driven Interactive Head Generation in Dyadic Conversations](https://mlsanity.com/paper/2412.04037/) (2024)
- [MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation](https://mlsanity.com/paper/2412.04448/) (2024)
- [Affective Faces for Goal-Driven Dyadic Communication](https://mlsanity.com/paper/2301.10939/) (2023)
- [Dyadic Interaction Modeling for Social Behavior Generation](https://mlsanity.com/paper/2403.09069/) (2024)
- [DialogueNeRF: Towards Realistic Avatar Face-to-Face Conversation Video Generation](https://mlsanity.com/paper/2203.07931/) (2022)
- [CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation](https://mlsanity.com/paper/2403.00274/) (2024)
- [MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model](https://mlsanity.com/paper/2308.16635/) (2023)
- [Interactive Conversational Head Generation](https://mlsanity.com/paper/2307.02090/) (2023)

## Built on by (most cited first, 16 of 16)

- [StreamAvatar: Streaming Diffusion Models for Real-Time Interactive Human Avatars](https://mlsanity.com/paper/2512.22065/) (2025)
- [Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation](https://mlsanity.com/paper/2601.00664/) (2026)
- [FlowAct-R1: Towards Interactive Humanoid Video Generation](https://mlsanity.com/paper/2601.10103/) (2026)
- [LPM 1.0: Video-based Character Performance Model](https://mlsanity.com/paper/2604.07823/) (2026)
- [DyStream: Streaming Dyadic Talking Heads Generation via Flow Matching-based Autoregressive Model](https://mlsanity.com/paper/2512.24408/) (2025)
- [Towards Seamless Interaction: Causal Turn-Level Modeling of Interactive 3D Conversational Head Dynamics](https://mlsanity.com/paper/2512.15340/) (2025)
- [Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length](https://mlsanity.com/paper/2512.04677/) (2025)
- [FluentAvatar: Flicker-Free Talking-Head Animation via Phoneme-Guided Autoregressive Modeling](https://mlsanity.com/paper/2509.12052/) (2025)
- [TAVID: Text-Driven Audio-Visual Interactive Dialogue Generation](https://mlsanity.com/paper/2512.20296/) (2025)
- [Towards Flexible, Natural, Efficient Interaction for Conversational Talking Face Generation](https://mlsanity.com/paper/2606.31088/) (2026)
- [Avatar-Forever: Decoupled Parallel Training for High-Quality Real-Time Infinite Avatars](https://mlsanity.com/paper/2608.12107/) (2026)
- [ECHO: Towards Emotionally Appropriate and Contextually Aware Interactive Head Generation](https://mlsanity.com/paper/2603.17427/) (2026)
- [MindFlow: Harmonizing Cognitive Semantics and Acoustic Dynamics for Facial Animation Generation in Dyadic Conversations](https://mlsanity.com/paper/2606.27779/) (2026)
- [AVTR-1: Open Stack for Real-Time Interactive Avatars](https://mlsanity.com/paper/2609.22913/) (2026)
- [EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold](https://mlsanity.com/paper/2609.35616/) (2026)
- [GLARE: Generating Listening Heads with Appropriate Reactions](https://mlsanity.com/paper/2609.40317/) (2026)

## Abstract (arXiv)

Face-to-face communication, as a common human activity, motivates the research on interactive head generation. A virtual agent can generate motion responses with both listening and speaking capabilities based on the audio or motion signals of the other user and itself. However, previous clip-wise generation paradigm or explicit listener/speaker generator-switching methods have limitations in future signal acquisition, contextual behavioral understanding, and switching smoothness, making it challenging to be real-time and realistic. In this paper, we propose an autoregressive (AR) based frame-wise framework called ARIG to realize the real-time generation with better interaction realism. To achieve real-time generation, we model motion prediction as a non-vector-quantized AR process. Unlike discrete codebook-index prediction, we represent motion distribution using diffusion procedure, achieving more accurate predictions in continuous space. To improve interaction realism, we emphasize interactive behavior understanding (IBU) and detailed conversational state understanding (CSU). In IBU, based on dual-track dual-modal signals, we summarize short-range behaviors through bidirectional-integrated learning and perform contextual understanding over long ranges. In CSU, we use voice activity signals and context features of IBU to understand the various states (interruption, feedback, pause, etc.) that exist in actual conversations. These serve as conditions for the final progressive motion prediction. Extensive experiments have verified the effectiveness of our model.