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Diffusion models hold significant appeal within the realm of synthetic media generation and demonstrate exceptional performance in personalized human image generation. However, the efficiency of the existing approaches is hindered by their limited capacity to handle images captured under different exposure conditions as the models tend to incorrectly attribute the illumination of an individual's facial region to their complexion. In addition, previous methodologies encounter challenges in the realm of posture transfer due to the fact that their image encoders primarily emphasize the extraction of character identity. We introduces ExpoGenius, a framework that facilitates efficient and personalized human image generation for exposure variation and pose transfer, all accomplished without the need for fine-tuning. ExpoGenius employs Multi-Grained Identity Feature to extract and combine facial features that are not affected by varying lighting conditions, enabling the accurate representation of facial attributes in various diverse exposure levels. To facilitate the transfer of pose, we propose Pose Feature Injection to inject pose feature into our improved diffusion model with identity feature and textual embeddings. In scenarios where facial images occupy a relatively small proportion within an overall image, the accuracy of identity extraction may encounter challenges. ExpoGenius presents Adaptive Facial Loss which effectively enhances the accuracy of identity extraction. Our research has substantiated the exceptional effectiveness of ExpoGenius in simultaneously preserving identity and posture in personalized human image generation tasks.
Liu et al. (Thu,) studied this question.