17 citations · 56 across the 8 of their papers we have counts for
8 papers
Color Pass-Through via Camera-Display Coupling
Ruikang Li, Molin Li, Jiarui Wu +3
When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and…
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…
Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors
Tao Yu, Zerong Zheng, Kaiwen Guo +3
Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real…
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…
Semantically Consistent Image Completion with Fine-grained Details
Pengpeng Liu, Xiaojuan Qi, Pinjia He +3
Image completion has achieved significant progress due to advances in generative adversarial networks (GANs). Albeit natural-looking, the synthesized contents still lack details, e…