133 citations · 363 across the 13 of their papers we have counts for
13 papers
TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
Kehong Gong, Dongze Lian, Heng Chang +5
We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Unlike existing works that generate dance movements using…
OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis
Hongyi Xu, Guoxian Song, Zihang Jiang +6
We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D he…
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning
Guoxian Song, Hongyi Xu, Jing Liu +7
While substantial progresses have been made in automated 2D portrait stylization, admirable 3D portrait stylization from a single user photo remains to be an unresolved challenge.…
AvatarGen: A 3D Generative Model for Animatable Human Avatars
Jianfeng Zhang, Zihang Jiang, Dingdong Yang +6
Unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries is important for creating virtual human avatars and other AR/VR applications…
AvatarGen: a 3D Generative Model for Animatable Human Avatars
Jianfeng Zhang, Zihang Jiang, Dingdong Yang +6
Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications. Existing met…
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Yujun Shi, Kuangqi Zhou, Jian Liang +5
Class Incremental Learning (CIL) aims at learning a multi-class classifier in a phase-by-phase manner, in which only data of a subset of the classes are provided at each phase. Pre…
VOLO: Vision Outlooker for Visual Recognition
Li Yuan, Qibin Hou, Zihang Jiang +2
Visual recognition has been dominated by convolutional neural networks (CNNs) for years. Though recently the prevailing vision transformers (ViTs) have shown great potential of sel…
LV-BERT: Exploiting Layer Variety for BERT
Weihao Yu, Zihang Jiang, Fei Chen +2
Modern pre-trained language models are mostly built upon backbones stacking self-attention and feed-forward layers in an interleaved order. In this paper, beyond this stereotyped l…
Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition
Qibin Hou, Zihang Jiang, Li Yuan +3
In this paper, we present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. By realizing the importance of the positional in…
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Li Yuan, Yunpeng Chen, Tao Wang +6
Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT mo…
ConvBERT: Improving BERT with Span-based Dynamic Convolution
Zihang Jiang, Weihao Yu, Daquan Zhou +3
Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural language understanding tasks. However, BERT heavily relies o…
Few-shot Classification via Adaptive Attention
Zihang Jiang, Bingyi Kang, Kuangqi Zhou +1
Training a neural network model that can quickly adapt to a new task is highly desirable yet challenging for few-shot learning problems. Recent few-shot learning methods mostly con…
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong +1
Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging…