State of the art on diffusion models for visual computing

R Po, W Yifan, V Golyanik, K Aberman… - Computer Graphics …, 2024 - Wiley Online Library
The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

Generative ai meets 3d: A survey on text-to-3d in aigc era

C Li, C Zhang, J Cho, A Waghwase, LH Lee… - arxiv preprint arxiv …, 2023 - arxiv.org
Generative AI has made significant progress in recent years, with text-guided content
generation being the most practical as it facilitates interaction between human instructions …

Human-Centered Interaction in Virtual Worlds: A New Era of Generative Artificial Intelligence and Metaverse

Y Wang, L Wang, KL Siau - International Journal of Human …, 2024 - Taylor & Francis
The metaverse has emerged as an exciting new paradigm for human-computer interaction
(HCI) and virtual collaboration. This paper presents a comprehensive review of the …

Dual memory networks: A versatile adaptation approach for vision-language models

Y Zhang, W Zhu, H Tang, Z Ma… - Proceedings of the …, 2024 - openaccess.thecvf.com
With the emergence of pre-trained vision-language models like CLIP how to adapt them to
various downstream classification tasks has garnered significant attention in recent …

Vpp: Efficient conditional 3d generation via voxel-point progressive representation

Z Qi, M Yu, R Dong, K Ma - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Conditional 3D generation is undergoing a significant advancement, enabling the free
creation of 3D content from inputs such as text or 2D images. However, previous …

Anyhome: Open-vocabulary generation of structured and textured 3d homes

R Fu, Z Wen, Z Liu, S Sridhar - European Conference on Computer Vision, 2024 - Springer
Inspired by cognitive theories, we introduce AnyHome, a framework that translates any text
into well-structured and textured indoor scenes at a house-scale. By prompting Large …

Advances in 3d generation: A survey

X Li, Q Zhang, D Kang, W Cheng, Y Gao… - arxiv preprint arxiv …, 2024 - arxiv.org
Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …

A survey on generative modeling with limited data, few shots, and zero shot

M Abdollahzadeh, T Malekzadeh, CTH Teo… - arxiv preprint arxiv …, 2023 - arxiv.org
In machine learning, generative modeling aims to learn to generate new data statistically
similar to the training data distribution. In this paper, we survey learning generative models …

Texgen: Text-guided 3d texture generation with multi-view sampling and resampling

D Huo, Z Guo, X Zuo, Z Shi, J Lu, P Dai, S Xu… - … on Computer Vision, 2024 - Springer
Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual
descriptions. Current methods for generating and assembling textures from sampled views …

Masked audio generation using a single non-autoregressive transformer

A Ziv, I Gat, GL Lan, T Remez, F Kreuk… - arxiv preprint arxiv …, 2024 - arxiv.org
We introduce MAGNeT, a masked generative sequence modeling method that operates
directly over several streams of audio tokens. Unlike prior work, MAGNeT is comprised of a …