Unsupervised generation of clothed virtual humans with various appearance and
animatable poses is important for creating 3D human avatars and other AR/VR applications …

Appearance and pose-guided human generation is a burgeoning field that has captured
significant attention. This subject's primary objective is to transfer pose information from a …

Existing self-supervised 3D human pose estimation schemes have largely relied on weak
supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior …

Recent advances in 3D-aware GAN models have enabled the generation of realistic and
controllable human body images. However, existing methods focus on the control of major …

In this work we develop a generalizable and efficient Neural Radiance Field (NeRF) pipeline
for high-fidelity free-viewpoint human body synthesis under settings with sparse camera …

Image and video synthesis has become a blooming topic in computer vision and machine
learning communities along with the developments of deep generative models, due to its …

Existing 3D human pose estimation methods often suffer inferior generalization performance
to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data …

Text-driven person image generation is an emerging and challenging task in cross-modality
image generation. Controllable person image generation promotes a wide range of …

Transformers have been recognized as powerful tools for various cross-modal tasks due to
their superior ability to perform representation learning through self-attention. Existing …

The pixel-level classification of multimodal remote sensing (RS) images plays a crucial role
in the intelligent interpretation of RS data. However, existing methods that mainly focus on …