StreamingEffect: Real-Time Human-Centric Video Effect Generation

Streaming video effect generation aims to add expressive, aesthetically coherent, and instruction-following effects to human-centric videos in real time, enabling live applications such as e-commerce streaming, entertainment, and vlogging. Unlike conventional video editing, this task requires not only temporal consistency and visual quality, but also low-latency generation under strict deployment constraints. In this paper, we introduce StreamingEffect, a new framework for real-time human-centric video effect generation. We formulate the task as a streaming video-to-video generation problem and adopt an in-context video editing architecture, which preserves source-video information more faithfully than prior conditioning schemes such as VACE-style designs, leading to stronger consistency and content preservation. To overcome the high cost of bidirectional diffusion-based generation, we further propose a two-stage distillation pipeline that first converts a high-quality bidirectional teacher into a causal autoregressive student, and then distills it from 50-step sampling to 4-step generation. We further introduce keyframe control, which allows high-quality image-edited keyframes to be injected and propagated online for interactive effect enhancement. To support this task, we present VideoEffect-60K, a large-scale dataset of 60K real high-quality videos covering 600 effect categories. Experiments show that our method achieves the first real-time 720P streaming video effect generation on a single H200 GPU while maintaining strong quality and controllability.