The method employed by DreamID-V involves pre-training the Identity-Anchored Video Synthesizer and combining it with DreamID to create customized injection mechanisms for Spatio-Temporal Context, Structural Guidance, and Identity Information. A three-stage training strategy is used, consisting of Synthetic Training, Real Augmentation Training, and Identity-Coherence Reinforcement Learning. This strategy enables the model to fully leverage the Bidirectional Quadruplet Pair data.
DreamID-V achieves high-fidelity face swapping in diverse scenarios, including significant face shape variations and different ethnicities. The technology is demonstrated through various video demos, showcasing its capabilities in handling complex face swapping tasks. The reference images and videos used in the demos are sourced from public domains or generated by models, highlighting the technology's potential applications in various fields.
