💡 The paper introduces FashionChameleon, a real-time and interactive framework for human-garment video customization in autoregressive video generation. The problem addressed is the inability of existing approaches to support low-latency and interactive garment control, which is crucial for applications such as e-commerce and content creation.

To solve this problem, the authors propose a method that consists of three key techniques. First, they train a Teacher Model with In-Context Learning on a single reference-garment pair, which encourages the model to implicitly preserve coherence during single-garment switching. Second, they introduce Streaming Distillation with In-Context Learning, which fine-tunes the model with in-context teacher forcing and improves extrapolation consistency via gradient-reweighted distribution matching distillation. Third, they propose Training-Free KV Cache Rescheduling, which includes garment KV refresh, historical KV withdraw, and reference KV disentangle to achieve garment switching while preserving motion coherence.

The results show that FashionChameleon uniquely supports interactive customization and consistent long-video extrapolation, while achieving real-time generation at 23.8 FPS on a single GPU. This is 30-180 times faster than existing baselines. The framework enables users to interactively switch garments during generation, making it a significant contribution to the field of human-centric video customization. Overall, the paper presents a novel approach to achieving real-time and interactive human-garment video customization, which has significant commercial value and potential applications in e-commerce and content creation.