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Recent progress in multi-modal conditioned face synthesis has enabled thecreation of visually striking and accurately aligned facial images. Yet,current methods still face issues with scalability, limited flexibility, and aone-size-fits-all approach to control strength, not accounting for thediffering levels of conditional entropy, a measure of unpredictability in datagiven some condition, across modalities. To address these challenges, weintroduce a novel uni-modal training approach with modal surrogates, coupledwith an entropy-aware modal-adaptive modulation, to support flexible, scalable,and scalable multi-modal conditioned face synthesis network. Our uni-modaltraining with modal surrogate that only leverage uni-modal data, use modalsurrogate to decorate condition with modal-specific characteristic and serve aslinker for inter-modal collaboration , fully learns each modality control inface synthesis process as well as inter-modal collaboration. The entropy-awaremodal-adaptive modulation finely adjust diffusion noise according tomodal-specific characteristics and given conditions, enabling well-informedstep along denoising trajectory and ultimately leading to synthesis results ofhigh fidelity and quality. Our framework improves multi-modal face synthesisunder various conditions, surpassing current methods in image quality andfidelity, as demonstrated by our thorough experimental results.

Towards Flexible, Scalable, and Adaptive Multi-Modal Conditioned Face Synthesis