We present DreamAvatar a text-and-shape guided framework for generating high-quality 3D
human avatars with controllable poses. While encouraging results have been reported by …

Realistic 3D human generation from text prompts is a desirable yet challenging task.
Existing methods optimize 3D representations like mesh or neural fields via score distillation …

Current methods for learning realistic and animatable 3D clothed avatars need either posed
3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn …

The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is
enabling the creation of detailed, clothed, 3D humans from images. However, existing …

This paper studies the human image animation task which aims to generate a video of a
certain reference identity following a particular motion sequence. Existing animation works …

Abstract Neural Radiance Field (NeRF) significantly degrades when only a limited number
of views are available. To complement the lack of 3D information, depth-based models, such …

We present GauHuman a 3D human model with Gaussian Splatting for both fast training (1 2
minutes) and real-time rendering (up to 189 FPS) compared with existing NeRF-based …

Recent text-to-3D methods employing diffusion models have made significant
advancements in 3D human generation. However these approaches face challenges due to …

Abstract 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D imagery …

Efficient generation of 3D digital humans is important in several industries including virtual
reality social media and cinematic production. 3D generative adversarial networks (GANs) …