SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads

• URL: http://arxiv.org/abs/2602.07449v3
• Date: Wed, 11 Feb 2026 12:34:52 GMT
• Title: SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads
• Authors: Tan Yu, Qian Qiao, Le Shen, Ke Zhou, Jincheng Hu, Dian Sheng, Bo Hu, Haoming Qin, Jun Gao, Changhai Zhou, Shunshun Yin, Siyuan Liu,
• Abstract summary: We propose SoulX-FlashHead, a unified framework for real-time, infinite-length, and high-fidelity streaming video generation.<n>To address the instability of audio features in streaming scenarios, we introduce Streaming-Aware Spatiotemporal Pre-training equipped with a Temporal Audio Context Cache mechanism.<n>We also present VividHead, a large-scale, high-quality dataset containing 782 hours of strictly aligned footage to support robust training.
• Score: 19.531644258572353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving a balance between high-fidelity visual quality and low-latency streaming remains a formidable challenge in audio-driven portrait generation. Existing large-scale models often suffer from prohibitive computational costs, while lightweight alternatives typically compromise on holistic facial representations and temporal stability. In this paper, we propose SoulX-FlashHead, a unified 1.3B-parameter framework designed for real-time, infinite-length, and high-fidelity streaming video generation. To address the instability of audio features in streaming scenarios, we introduce Streaming-Aware Spatiotemporal Pre-training equipped with a Temporal Audio Context Cache mechanism, which ensures robust feature extraction from short audio fragments. Furthermore, to mitigate the error accumulation and identity drift inherent in long-sequence autoregressive generation, we propose Oracle-Guided Bidirectional Distillation, leveraging ground-truth motion priors to provide precise physical guidance. We also present VividHead, a large-scale, high-quality dataset containing 782 hours of strictly aligned footage to support robust training. Extensive experiments demonstrate that SoulX-FlashHead achieves state-of-the-art performance on HDTF and VFHQ benchmarks. Notably, our Lite variant achieves an inference speed of 96 FPS on a single NVIDIA RTX 4090, facilitating ultra-fast interaction without sacrificing visual coherence.

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