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Found 46 papers, 16 papers with code
Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation
In the next stage, we use an object network to estimate the depth of those moving objects assuming rigid motions.
HashPoint: Accelerated Point Searching and Sampling for Neural Rendering
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.
MIDGET: Music Conditioned 3D Dance Generation
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
In this paper, we tackle the problem of scene-aware 3D human motion forecasting.
Variational Inference for Scalable 3D Object-centric Learning
We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes.
LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network
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.
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View
In contrast, we propose to use parametric depth distribution modeling for feature transformation.
VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos
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.
Sampled Transformer for Point Sets
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.
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's-Eye View
In contrast, we propose to use parametric depth distribution modeling for feature transformation.
K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring
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.
Contact-aware Human Motion Forecasting
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.
Ranked #2 on
Human Pose Forecasting
on GTA-IM Dataset
WiCV 2022: The Tenth Women In Computer Vision Workshop
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
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.
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
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.
SPA-VAE: Similar-Parts-Assignment for Unsupervised 3D Point Cloud Generation
This paper addresses the problem of unsupervised parts-aware point cloud generation with learned parts-based self-similarity.
Mining Meta-indicators of University Ranking: A Machine Learning Approach Based on SHAP
University evaluation and ranking is an extremely complex activity.
EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation
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.
Generating Smooth Pose Sequences for Diverse Human Motion Prediction
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
(ADE metric)
Multi-level Motion Attention for Human Motion Prediction
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.
Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition
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.
Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction
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
Here, we propose a self-supervised learning framework for multi-view stereo that exploit pseudo labels from the input data.
Fixed Viewpoint Mirror Surface Reconstruction under an Uncalibrated Camera
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.
Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration
The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches.
History Repeats Itself: Human Motion Prediction via Motion Attention
Human motion prediction aims to forecast future human poses given a past motion.
Single Image Optical Flow Estimation with an Event Camera
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.
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization
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.
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
We propose a cost volume-based neural network for depth inference from multi-view images.
Ranked #14 on
3D Reconstruction
on DTU
Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation
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).
Learning Trajectory Dependencies for Human Motion Prediction
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.
High Frame Rate Video Reconstruction based on an Event Camera
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.
Single Image Deblurring and Camera Motion Estimation with Depth Map
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.
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring
The image blurring process is generally modelled as the convolution of a blur kernel with a latent image.
Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera
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.
Geometry-aware Deep Network for Single-Image Novel View Synthesis
By contrast, in this paper, we propose to exploit the 3D geometry of the scene to synthesize a novel view.
Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps
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
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.
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
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.
Mirror Surface Reconstruction Under an Uncalibrated Camera
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
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.
Structured Depth Prediction in Challenging Monocular Video Sequences
To this end, we first study the problem of depth estimation from a single image.
Indoor Scene Structure Analysis for Single Image Depth Estimation
We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities.
A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects
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
The unary potentials in this graphical model are computed by making use of the images with known depth.
Mirror Surface Reconstruction from a Single Image
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.
