42 citations · 150 across the 10 of their papers we have counts for
10 papers
Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation
Pengpeng Liu, Michael R. Lyu, Irwin King +1
We present DistillFlow, a knowledge distillation approach to learning optical flow. DistillFlow trains multiple teacher models and a student model, where challenging transformation…
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling
Zhichao Huang, Xintong Han, Jia Xu +1
We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs. Despite recent advances in…
Learning 3D Face Reconstruction with a Pose Guidance Network
Pengpeng Liu, Xintong Han, Michael Lyu +2
We present a self-supervised learning approach to learning monocular 3D face reconstruction with a pose guidance network (PGN). First, we unveil the bottleneck of pose estimation i…
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching
Pengpeng Liu, Irwin King, Michael Lyu +1
In this paper, we propose a unified method to jointly learn optical flow and stereo matching. Our first intuition is stereo matching can be modeled as a special case of optical flo…
Learning to Solve a Rubik's Cube with a Dexterous Hand
Tingguang Li, Weitao Xi, Meng Fang +2
We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand. Despite the promising performance of dexterous in-hand manipulation, solving co…
SelFlow: Self-Supervised Learning of Optical Flow
Pengpeng Liu, Michael Lyu, Irwin King +1
We present a self-supervised learning approach for optical flow. Our method distills reliable flow estimations from non-occluded pixels, and uses these predictions as ground truth…
DDFlow: Learning Optical Flow with Unlabeled Data Distillation
Pengpeng Liu, Irwin King, Michael R. Lyu +1
We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data. The approach distills reliable predictions from a teacher network, and uses…
Learning to See in the Dark
Chen Chen, Qifeng Chen, Jia Xu +1
Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A va…
Fast Image Processing with Fully-Convolutional Networks
Qifeng Chen, Jia Xu, Vladlen Koltun
We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demo…
Accurate Optical Flow via Direct Cost Volume Processing
Jia Xu, René Ranftl, Vladlen Koltun
We present an optical flow estimation approach that operates on the full four-dimensional cost volume. This direct approach shares the structural benefits of leading stereo matchin…