# Teller: Real-Time Streaming Audio-Driven Portrait Animation with Autoregressive Motion Generation Source: [Teller: Real-Time Streaming Audio-Driven Portrait Animation with Autoregressive Motion Generation]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:20:00.372Z Authors: Dingcheng Zhen, Shunshun Yin, Shiyang Qin, Hou Yi, Ziwei Zhang, Siyuan Liu, Gan Qi, Ming Tao Published: 2025-03-24T08:16:47.000Z ## Abstract In this work, we introduce the first autoregressive framework for real-time, audio-driven portrait animation, a.k.a, talking head. Beyond the challenge of lengthy animation times, a critical challenge in realistic talking head generation lies in preserving the natural movement of diverse body parts. To this end, we propose Teller, the first streaming audio-driven protrait animation framework with autoregressive motion generation. Specifically, Teller first decomposes facial and body detail animation into two components: Facial Motion Latent Generation (FMLG) based on an autoregressive transfromer, and movement authenticity refinement using a Efficient Temporal Module (ETM).Concretely, FMLG employs a Residual VQ model to map the facial motion latent from the implicit keypoint-based model into discrete motion tokens, which are then temporally sliced with audio embeddings. This enables the AR tranformer to learn real-time, stream-based mappings from audio to motion. Furthermore, Teller incorporate ETM to capture finer motion details. This module ensures the physical consistency of body parts and accessories, such as neck muscles and earrings, improving the realism of these movements. Teller is designed to be efficient, surpassing the inference speed of diffusion-based models (Hallo 20.93s vs. Teller 0.92s for one second video generation), and achieves a real-time streaming performance of up to 25 FPS. Extensive experiments demonstrate that our method outperforms recent audio-driven portrait animation models, especially in small movements, as validated by human evaluations with a significant margin in quality and realism. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Portrait image animation on HDTF](): Portrait Image Animation; HDTF; Sync-C: 7.696. - [Portrait Image Animation on RAVDESS](): Portrait Image Animation; RAVDESS; Sync-C: 4.496. ## Original sources and implementation [Original paper]() [PDF]()