32 citations · 157 across the 45 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…