Search Results for author:
Found 6 papers, 1 papers with code
3D-LatentMapper: View Agnostic Single-View Reconstruction of 3D Shapes
Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to represent and generate 3D shapes, as well as a vast number of use cases.
XDGAN: Multi-Modal 3D Shape Generation in 2D Space
Specifically, we propose a novel method to convert 3D shapes into compact 1-channel geometry images and leverage StyleGAN3 and image-to-image translation networks to generate 3D objects in 2D space.
FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations
Our experiments show that our method successfully debiases the GAN model within a few minutes without compromising the quality of the generated images.
Text and Image Guided 3D Avatar Generation and Manipulation
In this work, we propose a novel 3D manipulation method that can manipulate both the shape and texture of the model using text or image-based prompts such as 'a young face' or 'a surprised face'.
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data.
Controlled Cue Generation for Play Scripts
In this paper, we use a large-scale play scripts dataset to propose the novel task of theatrical cue generation from dialogues.
