Search Results for author: Miaomiao Liu

Found 29 papers, 10 papers with code

EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation

no code implementations13 Oct 2021 Shidi Li, Miaomiao Liu, Christian Walder

We achieve this with a simple modification of the Variational Auto-Encoder which yields a joint model of the point cloud itself along with a schematic representation of it as a combination of shape primitives.

Point Cloud Generation

Generating Smooth Pose Sequences for Diverse Human Motion Prediction

1 code implementation ICCV 2021 Wei Mao, Miaomiao Liu, Mathieu Salzmann

Recent progress in stochastic motion prediction, i. e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts.

Human motion prediction motion prediction

Multi-level Motion Attention for Human Motion Prediction

1 code implementation17 Jun 2021 Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li

Whether based on recurrent or feed-forward neural networks, existing learning based methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities.

Human motion prediction motion prediction

Spatially Invariant Unsupervised 3D Object Segmentation with Graph Neural Networks

no code implementations10 Jun 2021 Tianyu Wang, Miaomiao Liu, Kee Siong Ng

In particular, we propose a framework, SPAIR3D, to model a point cloud as a spatial mixture model and jointly learn the multiple-object representation and segmentation in 3D via Variational Autoencoders (VAE).

Semantic Segmentation

Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction

no code implementations20 May 2021 Kai Han, Kwan-Yee K. Wong, Miaomiao Liu

We present a simple setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid, and develop a method for recovering the object surface through reconstructing and triangulating such incident light paths.

Self-supervised Learning of Depth Inference for Multi-view Stereo

1 code implementation CVPR 2021 Jiayu Yang, Jose M. Alvarez, Miaomiao Liu

Here, we propose a self-supervised learning framework for multi-view stereo that exploit pseudo labels from the input data.

Depth Estimation Image Reconstruction +1

Fixed Viewpoint Mirror Surface Reconstruction under an Uncalibrated Camera

1 code implementation23 Jan 2021 Kai Han, Miaomiao Liu, Dirk Schnieders, Kwan-Yee K. Wong

This paper addresses the problem of mirror surface reconstruction, and proposes a solution based on observing the reflections of a moving reference plane on the mirror surface.

Single Image Optical Flow Estimation with an Event Camera

no code implementations CVPR 2020 Liyuan Pan, Miaomiao Liu, Richard Hartley

Then, we consider the special case of image blur caused by high dynamics in the visual environments and show that including the blur formation in our model further constrains flow estimation.

Deblurring Optical Flow Estimation

Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization

no code implementations26 Feb 2020 Shihao Jiang, Dylan Campbell, Miaomiao Liu, Stephen Gould, Richard Hartley

We address the problem of joint optical flow and camera motion estimation in rigid scenes by incorporating geometric constraints into an unsupervised deep learning framework.

Motion Estimation Optical Flow Estimation

Cost Volume Pyramid Based Depth Inference for Multi-View Stereo

1 code implementation CVPR 2020 Jiayu Yang, Wei Mao, Jose M. Alvarez, Miaomiao Liu

We propose a cost volume-based neural network for depth inference from multi-view images.

Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation

no code implementations6 Oct 2019 Liyuan Pan, Yuchao Dai, Miaomiao Liu, Fatih Porikli, Quan Pan

Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes).

Deblurring Scene Flow Estimation +1

Learning Trajectory Dependencies for Human Motion Prediction

3 code implementations ICCV 2019 Wei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong Li

In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints.

Human motion prediction Human Pose Forecasting +2

High Frame Rate Video Reconstruction based on an Event Camera

1 code implementation12 Mar 2019 Liyuan Pan, Richard Hartley, Cedric Scheerlinck, Miaomiao Liu, Xin Yu, Yuchao Dai

Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos.

Video Generation Video Reconstruction

Single Image Deblurring and Camera Motion Estimation with Depth Map

no code implementations1 Mar 2019 Liyuan Pan, Yuchao Dai, Miaomiao Liu

Camera shake during exposure is a major problem in hand-held photography, as it causes image blur that destroys details in the captured images.~In the real world, such blur is mainly caused by both the camera motion and the complex scene structure.~While considerable existing approaches have been proposed based on various assumptions regarding the scene structure or the camera motion, few existing methods could handle the real 6 DoF camera motion.~In this paper, we propose to jointly estimate the 6 DoF camera motion and remove the non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with a single blurry image and its depth map (either direct depth measurements, or a learned depth map) as input.~We formulate our joint deblurring and 6 DoF camera motion estimation as an energy minimization problem which is solved in an alternative manner.

Deblurring Motion Estimation

Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera

1 code implementation CVPR 2019 Liyuan Pan, Cedric Scheerlinck, Xin Yu, Richard Hartley, Miaomiao Liu, Yuchao Dai

In this paper, we propose a simple and effective approach, the \textbf{Event-based Double Integral (EDI)} model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data.

Video Generation

Geometry-aware Deep Network for Single-Image Novel View Synthesis

no code implementations CVPR 2018 Miaomiao Liu, Xuming He, Mathieu Salzmann

By contrast, in this paper, we propose to exploit the 3D geometry of the scene to synthesize a novel view.

Novel View Synthesis

Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps

no code implementations27 Nov 2017 Liyuan Pan, Yuchao Dai, Miaomiao Liu, Fatih Porikli

In this paper, we propose to tackle the problem of depth map completion by jointly exploiting the blurry color image sequences and the sparse depth map measurements, and present an energy minimization based formulation to simultaneously complete the depth maps, estimate the scene flow and deblur the color images.

Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference

no code implementations CVPR 2017 Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu

In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships.

Instance Segmentation Scene Parsing +1

Simultaneous Stereo Video Deblurring and Scene Flow Estimation

no code implementations CVPR 2017 Liyuan Pan, Yuchao Dai, Miaomiao Liu, Fatih Porikli

Unlike the existing approach [31] which used a pre-computed scene flow, we propose a single framework to jointly estimate the scene flow and deblur the image, where the motion cues from scene flow estimation and blur information could reinforce each other, and produce superior results than the conventional scene flow estimation or stereo deblurring methods.

Deblurring Scene Flow Estimation

Mirror Surface Reconstruction Under an Uncalibrated Camera

no code implementations CVPR 2016 Kai Han, Kwan-Yee K. Wong, Dirk Schnieders, Miaomiao Liu

Unlike previous approaches which require tedious work to calibrate the camera, our method can recover both the camera intrinsics and extrinsics together with the mirror surface from reflections of the reference plane under at least three unknown distinct poses.

Semantic-Aware Depth Super-Resolution in Outdoor Scenes

no code implementations31 May 2016 Miaomiao Liu, Mathieu Salzmann, Xuming He

Despite much progress, state-of-the-art techniques suffer from two drawbacks: (i) they rely on the assumption that intensity edges coincide with depth discontinuities, which, unfortunately, is only true in controlled environments; and (ii) they typically exploit the availability of high-resolution training depth maps, which can often not be acquired in practice due to the sensors' limitations.

Super-Resolution

Indoor Scene Structure Analysis for Single Image Depth Estimation

no code implementations CVPR 2015 Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu

We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities.

Depth Estimation

A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects

no code implementations CVPR 2015 Kai Han, Kwan-Yee K. Wong, Miaomiao Liu

In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light.

Discrete-Continuous Depth Estimation from a Single Image

no code implementations CVPR 2014 Miaomiao Liu, Mathieu Salzmann, Xuming He

The unary potentials in this graphical model are computed by making use of the images with known depth.

Monocular Depth Estimation

Mirror Surface Reconstruction from a Single Image

no code implementations CVPR 2013 Miaomiao Liu, Richard Hartley, Mathieu Salzmann

In such conditions, our differential geometry analysis provides a theoretical proof that the shape of the mirror surface can be uniquely recovered if the pose of the reference target is known.

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