32 citations · 157 across the 45 of their papers we have counts for
9 papers · 1 filter
Meta ControlNet: Enhancing Task Adaptation via Meta Learning
Junjie Yang, Jinze Zhao, Peihao Wang +2
Diffusion-based image synthesis has attracted extensive attention recently. In particular, ControlNet that uses image-based prompts exhibits powerful capability in image tasks such…
Growth of high-quality CrI3 single crystals and engineering of its magnetic properties via V and Mn doping
Shuang Pan, Yuqing Bai, Jiaxuan Tang +4
CrI3, as a soft van der Waals layered magnetic material, has been widely concerned and explored for its magnetic complexity and tunability. In this work, high quality and large siz…
Pose-Free Generalizable Rendering Transformer
Zhiwen Fan, Panwang Pan, Peihao Wang +6
In the field of novel-view synthesis, the necessity of knowing camera poses (e.g., via Structure from Motion) before rendering has been a common practice. However, the consistent a…
Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts
Wenyan Cong, Hanxue Liang, Peihao Wang +5
Cross-scene generalizable NeRF models, which can directly synthesize novel views of unseen scenes, have become a new spotlight of the NeRF field. Several existing attempts rely on…
Polynomial Width is Sufficient for Set Representation with High-dimensional Features
Peihao Wang, Shenghao Yang, Shu Li +2
Set representation has become ubiquitous in deep learning for modeling the inductive bias of neural networks that are insensitive to the input order. DeepSets is the most widely us…
Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images
Panwang Pan, Zhiwen Fan, Brandon Y. Feng +3
The accurate estimation of six degrees-of-freedom (6DoF) object poses is essential for many applications in robotics and augmented reality. However, existing methods for 6DoF pose…
POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference
Zhiwen Fan, Panwang Pan, Peihao Wang +4
Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation, existing methods have limited applicability in real-world scenarios involving embodied age…
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Zhendong Wang, Yifan Jiang, Huangjie Zheng +5
Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the tr…
Learning to Grow Pretrained Models for Efficient Transformer Training
Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen +6
Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic…