EchoTorrent: Towards Swift, Sustained, and Streaming Multi-Modal Video Generation

• URL: http://arxiv.org/abs/2602.13669v1
• Date: Sat, 14 Feb 2026 08:32:38 GMT
• Title: EchoTorrent: Towards Swift, Sustained, and Streaming Multi-Modal Video Generation
• Authors: Rang Meng, Weipeng Wu, Yingjie Yin, Yuming Li, Chenguang Ma,
• Abstract summary: Multi-modal generation models have achieved high visual quality, but their prohibitive latency and limited temporal stability hinder real-time deployment.<n> Streaming inference exacerbates these issues, leading to pronounced multimodal ambiguities, such as blurring, temporal drift, and lip dechronization.<n>We propose EchoTorrent, a novel novel with a fourfold schema: Multi-Teacher Training fine-tunes a pre-trained model on distinct preference domains; Adaptive DMD (ACCDMD) calibrates the audio CFG degradation errors in phased via a schedule; Long Hybrid Tail, which enforces alignment exclusively on tail frames during long-horizon self-roll
• Score: 8.795438456031512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent multi-modal video generation models have achieved high visual quality, but their prohibitive latency and limited temporal stability hinder real-time deployment. Streaming inference exacerbates these issues, leading to pronounced multimodal degradation, such as spatial blurring, temporal drift, and lip desynchronization, which creates an unresolved efficiency-performance trade-off. To this end, we propose EchoTorrent, a novel schema with a fourfold design: (1) Multi-Teacher Training fine-tunes a pre-trained model on distinct preference domains to obtain specialized domain experts, which sequentially transfer domain-specific knowledge to a student model; (2) Adaptive CFG Calibration (ACC-DMD), which calibrates the audio CFG augmentation errors in DMD via a phased spatiotemporal schedule, eliminating redundant CFG computations and enabling single-pass inference per step; (3) Hybrid Long Tail Forcing, which enforces alignment exclusively on tail frames during long-horizon self-rollout training via a causal-bidirectional hybrid architecture, effectively mitigates spatiotemporal degradation in streaming mode while enhancing fidelity to reference frames; and (4) VAE Decoder Refiner through pixel-domain optimization of the VAE decoder to recover high-frequency details while circumventing latent-space ambiguities. Extensive experiments and analysis demonstrate that EchoTorrent achieves few-pass autoregressive generation with substantially extended temporal consistency, identity preservation, and audio-lip synchronization.

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