GLARE: Generating Listening Heads with Appropriate Reactions

arXiv:2609.40317 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/lzk901372/glare

Terminology

• Alignment — 17× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Consistency — 8× in this paper · explained in 3 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Verification — 7× in this paper · explained in 2 episodes
• Flow Matching — 5× in this paper · explained in 8 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Ablation study — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Latent Representation — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Human Agreement Rate — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal approach — 1× in this paper · explained in 2 episodes
• Peak Signal-to-Noise Ratio — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset
• DyStream: Streaming Dyadic Talking Heads Generation via Flow Matching-based Autoregressive Model
• Py-Feat: Python Facial Expression Analysis Toolbox
• Qwen2-Audio Technical Report
• Affective Faces for Goal-Driven Dyadic Communication
• Flow Matching for Generative Modeling
• MediaPipe: A Framework for Building Perception Pipelines
• InterDyad: Interactive Dyadic Speech-to-Video Generation by Querying Intermediate Visual Guidance
• Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
• Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• Latent Image Animator: Learning to Animate Images via Latent Space Navigation
• SpeakerVid-5M: A Large-Scale High-Quality Dataset for Audio-Visual Dyadic Interactive Human Generation

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