StreamingEffect: Real-Time Human-Centric Video Effect Generation
Streaming video effect generation is highly desirable for live human-centric applications such as e-commerce streaming, entertainment, and vlogging, yet remains difficult due to the lack of suitable data and deployable editing models. Unlike generic video generation, this task requires real-time video-to-video editing that adds expressive effects while preserving human identity, background content, and temporal consistency. Existing acceleration efforts mainly focus on text-to-video generation, while efficient distillation for video editing remains largely underexplored. In this paper, we present \textbf{StreamingEffect}, a real-time human-centric streaming video effect framework. We adop...
| Authors | Yiren Song, Cheng Liu, Yuxin Jiang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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