# CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation

arXiv: https://arxiv.org/abs/2403.00274 · HTML: https://mlsanity.com/paper/2403.00274/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 48 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss](https://mlsanity.com/paper/1905.03820/) (2019)
- [FLAME: Free-form Language-based Motion Synthesis & Editing](https://mlsanity.com/paper/2209.00349/) (2022)
- [PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering](https://mlsanity.com/paper/2109.08379/) (2021)
- [Long-term Human Motion Prediction with Scene Context](https://mlsanity.com/paper/2007.03672/) (2020)
- [MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement](https://mlsanity.com/paper/2104.08223/) (2021)
- [Audio-Driven Emotional Video Portraits](https://mlsanity.com/paper/2104.07452/) (2021)
- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [Fine-Grained Head Pose Estimation Without Keypoints](https://mlsanity.com/paper/1710.00925/) (2017)
- [Realistic Speech-Driven Facial Animation with GANs](https://mlsanity.com/paper/1906.06337/) (2019)
- [Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion](https://mlsanity.com/paper/2204.08451/) (2022)
- [Responsive Listening Head Generation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2112.13548/) (2021)
- [Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors](https://mlsanity.com/paper/2212.04248/) (2022)
- [Affective Faces for Goal-Driven Dyadic Communication](https://mlsanity.com/paper/2301.10939/) (2023)
- [TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles](https://mlsanity.com/paper/2304.00334/) (2023)
- [Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Sampling](https://mlsanity.com/paper/2308.01850/) (2023)
- [LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis](https://mlsanity.com/paper/2308.10713/) (2023)
- [MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model](https://mlsanity.com/paper/2308.16635/) (2023)
- [Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions](https://mlsanity.com/paper/1801.09092/) (2018)
- [High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptation](https://mlsanity.com/paper/2103.15876/) (2021)

## Built on by (most cited first, 22 of 22)

- [INFP: Audio-Driven Interactive Head Generation in Dyadic Conversations](https://mlsanity.com/paper/2412.04037/) (2024)
- [DualTalk: Dual-Speaker Interaction for 3D Talking Head Conversations](https://mlsanity.com/paper/2505.18096/) (2025)
- [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)
- [X-Streamer: Unified Human World Modeling with Audiovisual Interaction](https://mlsanity.com/paper/2509.21574/) (2025)
- [ARIG: Autoregressive Interactive Head Generation for Real-time Conversations](https://mlsanity.com/paper/2507.00472/) (2025)
- [UniLS: End-to-End Audio-Driven Avatars for Unified Listening and Speaking](https://mlsanity.com/paper/2512.09327/) (2025)
- [DiTaiListener: Controllable High Fidelity Listener Video Generation with Diffusion](https://mlsanity.com/paper/2504.04010/) (2025)
- [Towards Seamless Interaction: Causal Turn-Level Modeling of Interactive 3D Conversational Head Dynamics](https://mlsanity.com/paper/2512.15340/) (2025)
- [VividListener: Expressive and Controllable Listener Dynamics Modeling for Multi-Modal Responsive Interaction](https://mlsanity.com/paper/2504.21718/) (2025)
- [Multi-human Interactive Talking Dataset](https://mlsanity.com/paper/2508.03050/) (2025)
- [TAVID: Text-Driven Audio-Visual Interactive Dialogue Generation](https://mlsanity.com/paper/2512.20296/) (2025)
- [Warm Chat: Diffuse Emotion-aware Interactive Talking Head Avatar with Tree-Structured Guidance](https://mlsanity.com/paper/2508.18337/) (2025)
- [A Survey of Body and Face Motion: Datasets, Performance Evaluation Metrics and Generative Techniques](https://mlsanity.com/paper/2512.09005/) (2025)
- [Facial Expression Generation Aligned with Human Preference for Natural Dyadic Interaction](https://mlsanity.com/paper/2603.07093/) (2026)
- [Talking Together: Synthesizing Co-Located 3D Conversations from Audio](https://mlsanity.com/paper/2603.08674/) (2026)
- [ECHO: Towards Emotionally Appropriate and Contextually Aware Interactive Head Generation](https://mlsanity.com/paper/2603.17427/) (2026)
- [GDPO-Listener: Expressive Interactive Head Generation via Auto-Regressive Flow Matching and Group reward-Decoupled Policy Optimization](https://mlsanity.com/paper/2603.25020/) (2026)
- [MindFlow: Harmonizing Cognitive Semantics and Acoustic Dynamics for Facial Animation Generation in Dyadic Conversations](https://mlsanity.com/paper/2606.27779/) (2026)
- [STEER: Steerable Dyadic Head Avatars](https://mlsanity.com/paper/2607.23840/) (2026)
- [InterSing: Explicit Interaction Dynamics for 3D Duet Singing Animation and Beyond](https://mlsanity.com/paper/2609.04903/) (2026)
- [ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reaction](https://mlsanity.com/paper/2609.05506/) (2026)

## Abstract (arXiv)

Listening head generation aims to synthesize a non-verbal responsive listener head by modeling the correlation between the speaker and the listener in dynamic conversion.The applications of listener agent generation in virtual interaction have promoted many works achieving the diverse and fine-grained motion generation. However, they can only manipulate motions through simple emotional labels, but cannot freely control the listener's motions. Since listener agents should have human-like attributes (e.g. identity, personality) which can be freely customized by users, this limits their realism. In this paper, we propose a user-friendly framework called CustomListener to realize the free-form text prior guided listener generation. To achieve speaker-listener coordination, we design a Static to Dynamic Portrait module (SDP), which interacts with speaker information to transform static text into dynamic portrait token with completion rhythm and amplitude information. To achieve coherence between segments, we design a Past Guided Generation Module (PGG) to maintain the consistency of customized listener attributes through the motion prior, and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation. To train and evaluate our model, we have constructed two text-annotated listening head datasets based on ViCo and RealTalk, which provide text-video paired labels. Extensive experiments have verified the effectiveness of our model.