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Found 6 papers, 3 papers with code
Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention
Specifically, we insert parallel attention matrices to each cross-attention module in the denoising network, which attends to features extracted from reference images by an identity encoder.
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity.
Extracting Semantic Knowledge from GANs with Unsupervised Learning
Second, we train image-to-image translation networks on the synthesized datasets, enabling semantic-conditional image synthesis without human annotations.
Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models
Large-scale pre-trained language models have achieved great success on natural language generation tasks.
Linear Semantics in Generative Adversarial Networks
Given a trained GAN and as few as eight semantic annotations, the user is able to generate diverse images subject to a user-provided semantic layout, and control the synthesized image semantics.
Frame Difference-Based Temporal Loss for Video Stylization
To ensure temporal inconsistency between the frames of the stylized video, a common approach is to estimate the optic flow of the pixels in the original video and make the generated pixels match the estimated optical flow.
