Search Results for author: Miaomiao Liu

Found 47 papers, 18 papers with code

SOAF: Scene Occlusion-aware Neural Acoustic Field

no code implementations2 Jul 2024 Huiyu Gao, Jiahao Ma, David Ahmedt-Aristizabal, Chuong Nguyen, Miaomiao Liu

In this work, we propose a new approach called Scene Occlusion-aware Acoustic Field (SOAF) for accurate sound generation.

Audio Generation

HashPoint: Accelerated Point Searching and Sampling for Neural Rendering

no code implementations CVPR 2024 Jiahao Ma, Miaomiao Liu, David Ahmedt-Aristizaba, Chuong Nguyen

We solve this problem by our HashPoint method combining these two strategies, leveraging rasterization for efficient point searching and sampling, and ray marching for rendering.

Neural Rendering

MIDGET: Music Conditioned 3D Dance Generation

no code implementations18 Apr 2024 Jinwu Wang, Wei Mao, Miaomiao Liu

In this paper, we introduce a MusIc conditioned 3D Dance GEneraTion model, named MIDGET based on Dance motion Vector Quantised Variational AutoEncoder (VQ-VAE) model and Motion Generative Pre-Training (GPT) model to generate vibrant and highquality dances that match the music rhythm.

Scene-aware Human Motion Forecasting via Mutual Distance Prediction

1 code implementation1 Oct 2023 Chaoyue Xing, Wei Mao, Miaomiao Liu

In this paper, we tackle the problem of scene-aware 3D human motion forecasting.

Motion Forecasting

Variational Inference for Scalable 3D Object-centric Learning

no code implementations25 Sep 2023 Tianyu Wang, Kee Siong Ng, Miaomiao Liu

We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes.

Object Representation Learning +1

LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network

no code implementations CVPR 2024 Hao Yang, Liyuan Pan, Yan Yang, Richard Hartley, Miaomiao Liu

In this paper, we propose, to the best of our knowledge, the first framework that introduces the contrastive language-image pre-training framework (CLIP) to accurately estimate the blur map from a DP pair unsupervisedly.

Deblurring Image Defocus Deblurring

VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos

1 code implementation CVPR 2023 Huiyu Gao, Wei Mao, Miaomiao Liu

Different from their works which sparsify voxels globally with a fixed occupancy threshold, we perform the sparsification on a local feature volume along each visual ray to preserve at least one voxel per ray for more fine details.

3D Scene Reconstruction

Sampled Transformer for Point Sets

no code implementations28 Feb 2023 Shidi Li, Christian Walder, Alexander Soen, Lexing Xie, Miaomiao Liu

The sparse transformer can reduce the computational complexity of the self-attention layers to $O(n)$, whilst still being a universal approximator of continuous sequence-to-sequence functions.

Inductive Bias

K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring

no code implementations CVPR 2023 Yan Yang, Liyuan Pan, Liu Liu, Miaomiao Liu

It estimates a disparity feature map, which is used to query a trainable kernel set to estimate a blur kernel that best describes the spatially-varying blur.

Deblurring Image Restoration

Contact-aware Human Motion Forecasting

1 code implementation8 Oct 2022 Wei Mao, Miaomiao Liu, Richard Hartley, Mathieu Salzmann

In this paper, we tackle the task of scene-aware 3D human motion forecasting, which consists of predicting future human poses given a 3D scene and a past human motion.

Human Pose Forecasting Motion Forecasting

WiCV 2022: The Tenth Women In Computer Vision Workshop

no code implementations24 Aug 2022 Doris Antensteiner, Silvia Bucci, Arushi Goel, Marah Halawa, Niveditha Kalavakonda, Tejaswi Kasarla, Miaomiao Liu, Nermin Samet, Ivaxi Sheth

In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana.

Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction

1 code implementation CVPR 2022 Wei Mao, Miaomiao Liu, Mathieu Salzmann

We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history.

motion prediction Stochastic Human Motion Prediction

Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo

1 code implementation CVPR 2022 Jiayu Yang, Jose M. Alvarez, Miaomiao Liu

Boundary pixels usually follow a multi-modal distribution as they represent different depths; Therefore, the assumption results in an erroneous depth prediction at the coarser level of the cost volume pyramid and can not be corrected in the refinement levels leading to wrong depth predictions.

Depth Estimation Depth Prediction

SPA-VAE: Similar-Parts-Assignment for Unsupervised 3D Point Cloud Generation

no code implementations15 Mar 2022 Shidi Li, Christian Walder, Miaomiao Liu

This paper addresses the problem of unsupervised parts-aware point cloud generation with learned parts-based self-similarity.

Point Cloud Generation Single Particle Analysis

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.

Inductive Bias 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.

Ranked #2 on Human Pose Forecasting on AMASS (APD metric)

Diversity Human motion prediction +3

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-Centric Learning and Scene Decomposition

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

Experimental results demonstrate that SPAIR3D has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.

Object Relational Reasoning +1

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.

Object Surface Reconstruction +1

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.

Surface Reconstruction

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 Image Deblurring +1

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

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

5 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 +1

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 Image Deblurring +1

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.

Surface Reconstruction

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

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.

Object Surface Reconstruction +1

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

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.

Surface Reconstruction

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