Improving Identity Preservation in Video Generation with Multi-Branch Models

摘要

Identity preservation is a critical capability in video generation and one of the core requirements for high-quality video synthesis. Existing approaches typically extract facial features from reference images as conditional inputs and inject them into the generation pipeline to maintain subject identity. However, in the IPVG Challenge 2025, state-of-the-art models such as ConcatID still fall short of delivering satisfactory identity preservation. To address this limitation, we propose a simple yet highly effective multi-branch video generation framework based on entity routing. Concretely, we integrate several fine-tuned dedicated models to compensate for the base model's weaknesses in identity preservation, dynamically selecting the appropriate branch according to each prompt. In addition, we employ enhanced prompts to further steer the generation process. Remarkably, using just a single NVIDIA RTX 3090 GPU for 120 hours of training, we boost the baseline's cur_score from 0.242 to 0.313.