Neural Information Processing Systems http://nips.cc/

Assessment (IEA) is now being accorded serious attention.

However, such a solution often shows unsatisfactory editability on the source image. To address this, we propose DreamSteerer, a plug-in method for augmenting existing T2I personalization methods. Specifically, we enhance the source image conditioned editability of a personalized diffusion model via a novel Editability Driven Score Distillation (EDSD) objective. Moreover, we identify amodetrapping issuewithEDSD, andpropose amodeshifting regularization with spatial feature guided sampling to avoid such an issue.

As shown in the left experiments, for a given target pose, adjusting the composition of the same human in the reference image ( i .

In this work, we present MuDI, a novel framework that enables multi-subject personalization by effectively decoupling identities from multiple subjects.

However error control for inverse graphics is difficult, and inverse graphics methods can represent only the effects of the chosen intrinsics.

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.