Search Results for author:
Found 7 papers, 2 papers with code
Diffusion Models are Geometry Critics: Single Image 3D Editing Using Pre-Trained Diffusion Priors
We propose a novel image editing technique that enables 3D manipulations on single images, such as object rotation and translation.
AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections
For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations.
3D-aware Image Generation using 2D Diffusion Models
In this paper, we introduce a novel 3D-aware image generation method that leverages 2D diffusion models.
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds
This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 1024X1024) while keeping strict 3D consistency as in volume rendering.
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation
3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses.
