Abstract: A method and a system for performing image editing based on an attribute-specific text prompt includes acquiring a noise code (z), a textual instruction (Ai) specifying a target facial attribute to be edited, and a target camera pose (pt). Upon acquiring, mapping the noise code (z) to a latent code (w), via a mapping network. Once the mapping is done, editing the latent code (w) based on the textual instruction (Ai) to generate an edited latent code (?), via a text-driven Latent Attribute Editor (LAE). Further, based on the edited latent code (?), generating a color texture image and a set of alpha maps via a three-dimensional Generative Adversarial Network (3D GAN). Furthermore, based on the color texture image and the set of alpha maps, generating a 3D-aware and view-consistent image at the target camera pose (pt) via a differentiable renderer.

Abstract: A method and a system for performing image editing based on an attribute-specific text prompt includes acquiring a noise code (z), a textual instruction (Ai) specifying a target facial attribute to be edited, and a target camera pose (pt). Upon acquiring, mapping the noise code (z) to a latent code (w), via a mapping network. Once the mapping is done, editing the latent code (w) based on the textual instruction (Ai) to generate an edited latent code (?), via a text-driven Latent Attribute Editor (LAE). Further, based on the edited latent code (?), generating a color texture image and a set of alpha maps via a three-dimensional Generative Adversarial Network (3D GAN). Furthermore, based on the color texture image and the set of alpha maps, generating a 3D-aware and view-consistent image at the target camera pose (pt) via a differentiable renderer.

Abstract: An apparatus, computer readable storage medium and method of generating a diverse set of images from few-shot images, includes a parameter input receiving values for control parameters to control an extent to which each reference image impacts a newly generated image. The apparatus involves an image generation deep learning network for generating an image for each of the values for the control parameters. The deep learning network has an encoder, a transformer-based fusion block, and a decoder. The transformer-based fusion block includes a mapping network that computes meta-weights from features extracted from the reference images and the control parameters, and a cross-attention block to generate modulation weights based on the meta-weights. An output displays high-quality and diverse images generated based on the values for the control parameter.

Abstract: An apparatus, computer readable storage medium and method of generating a diverse set of images from few-shot images, includes a parameter input receiving values for control parameters to control an extent to which each reference image impacts a newly generated image. The apparatus involves an image generation deep learning network for generating an image for each of the values for the control parameters. The deep learning network has an encoder, a transformer-based fusion block, and a decoder. The transformer-based fusion block includes a mapping network that computes meta-weights from features extracted from the reference images and the control parameters, and a cross-attention block to generate modulation weights based on the meta-weights. An output displays high-quality and diverse images generated based on the values for the control parameter.