39 citations · 74 across the 6 of their papers we have counts for
9 papers · 1 filter
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…
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…