The Vision and Cognitive Computing lab focuses on designing artificial intelligence to perceive, represent, and model human-centric 3D worlds. Research is conducted through computer vision, computer graphics, and robotics.

본 연구실에서는 컴퓨터 비전, 컴퓨터 그래픽스 및 로봇 공학을 통해 인간 중심의 3D 세계를 인식, 표현 및 모델링하는 인공 지능 설계에 중점을 둔다.

Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image

Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator

PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image

PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image

Codec Avatar Studio: Paired Human Captures for Complete, Driveable, and Generalizable Avatars

Expressive Whole-Body 3D Gaussian Avatar

3D Hand Sequence Recovery from Real Blurry Images and Event Stream

Authentic Hand Avatar from a Phone Scan via Universal Hand Model

URHand: Universal Relightable Hands

Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer

A Dataset of Relighted 3D Interacting Hands

Bringing Inputs to Shared Domains for 3D Interacting Hands Recovery in the Wild

Recovering 3D Hand Mesh Sequence from a Single Blurry Image: A New Dataset and Temporal Unfolding

Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation

MultiAct: Long-Term 3D Human Motion Generation from Multiple Action Labels

MonoNHR: Monocular Neural Human Renderer

3D Clothed Human Reconstruction in the Wild

HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network

Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes

Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation

NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets

Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video

IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos

DeepHandMesh: A Weakly-Supervised Deep Encoder-Decoder Framework for High-Fidelity Hand Mesh Modeling

InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image

I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image

Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose

Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image

PoseFix: Model-agnostic General Human Pose Refinement Network

V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Depth-based 3D Hand Pose Estimation: From Current Achievements to Future Goals

NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study

Expressive whole-body 3D gaussian avatar

DeepHandMesh: A Weakly-supervised Deep Encoder-Decoder Framework for High-fidelity Hand Mesh Modeling

A dataset of relighted 3D interacting hands

NTIRE 2019 Challenge on Video Super-resolution: Methods and Results

Kyoung Mu Lee

Hongsuk Choi

Ju Yong Chang

Takaaki Shiratori

Seokil Hong

Radu Timofte

Seungjun Nah

Sungyong Baik

Sanghyun Son

He Wen

Hyeongjin Nam

Yeonguk Oh

Shunsuke Saito

Weipeng Xu

Shoou-I Yu

Taeryung Lee

JoonKyu Park

Daniel Sungho Jung

Heeseung Kwon

Minsu Cho

Gyeongsik Moon

This paper introduces a novel large dataset for video deblurring, video super-resolution and studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges. The video deblurring and video super-resolution challenges are each the first challenge of its kind, with 4 competitions, hundreds of participants and tens of proposed solutions. Our newly collected REalistic and Diverse Scenes dataset (REDS) was employed by the challenges. In our study, we compare the solutions from the challenges to a set of representative methods from the literature and evaluate them on our proposed REDS dataset. We find that the NTIRE 2019 challenges push the state-of-the-art in video deblurring and super-resolution, reaching compelling performance on our newly proposed REDS dataset.

Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in https://github. com/mks0601/V2V-PoseNet_RELEASE.

Most of the previous image-based 3D human pose and mesh estimation methods estimate parameters of the human mesh model from an input image. However, directly regressing the parameters from the input image is a highly non-linear mapping because it breaks the spatial relationship between pixels in the input image. In addition, it cannot model the prediction uncertainty, which can make training harder. To resolve the above issues, we propose I2L-MeshNet, an image-to-lixel (line+pixel) prediction network. The proposed I2L-MeshNet predicts the per-lixel likelihood on 1D heatmaps for each mesh vertex coordinate instead of directly regressing the parameters. Our lixel-based 1D heatmap preserves the spatial relationship in the input image and models the prediction uncertainty. We demonstrate the benefit of the image-to-lixel prediction and show that the proposed I2L-MeshNet outperforms previous methods …

Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image. The first weakness of these methods is the overfitting to image appearance, due to the domain gap between the training data captured from controlled settings such as a lab, and in-the-wild data in inference time. The second weakness is that the estimation of the pose parameters is quite challenging due to the representation issues of 3D rotations. To overcome the above weaknesses, we propose Pose2Mesh, a novel graph convolutional neural network (GraphCNN)-based system that estimates the 3D coordinates of human mesh vertices directly from the 2D human pose . The 2D human pose as input provides essential human body articulation information without image appearance. Also, the proposed system …

Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of human detection, absolute 3D human root localization, and root-relative 3D single-person pose estimation modules. Our system achieves comparable results with the state-of-the-art 3D single-person pose estimation models without any groundtruth information and significantly outperforms previous 3D multi-person pose estimation methods on publicly available datasets. The code is available in (https://github. com/mks0601/3DMPPE_ROOTNET_RELEASE),(https://github. com/mks0601/3DMPPE_POSENET_RELEASE).

Analysis of hand-hand interactions is a crucial step towards better understanding human behavior. However, most researches in 3D hand pose estimation have focused on the isolated single hand case. Therefore, we firstly propose (1) a large-scale dataset, InterHand2.6M, and (2) a baseline network, InterNet, for 3D interacting hand pose estimation from a single RGB image. The proposed InterHand2.6M consists of 2.6 M labeled single and interacting hand frames under various poses from multiple subjects. Our InterNet simultaneously performs 3D single and interacting hand pose estimation. In our experiments, we demonstrate big gains in 3D interacting hand pose estimation accuracy when leveraging the interacting hand data in InterHand2.6M. We also report the accuracy of InterNet on InterHand2.6M, which serves as a strong baseline for this new dataset. Finally, we show 3D interacting hand pose estimation …

Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed; however, they fail to resolve the single image-based methods' temporal inconsistency issue due to a strong dependency on a static feature of the current frame. In this regard, we present a temporally consistent mesh recovery system (TCMR). It effectively focuses on the past and future frames' temporal information without being dominated by the current static feature. Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy. We also release the codes.

In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include:(1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points;(2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data;(3) Discriminative methods still generalize poorly to unseen hand shapes;(4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.

Multi-person pose estimation from a 2D image is an essential technique for human behavior understanding. In this paper, we propose a human pose refinement network that estimates a refined pose from a tuple of an input image and input pose. The pose refinement was performed mainly through an end-to-end trainable multi-stage architecture in previous methods. However, they are highly dependent on pose estimation models and require careful model design. By contrast, we propose a model-agnostic pose refinement method. According to a recent study, state-of-the-art 2D human pose estimation methods have similar error distributions. We use this error statistics as prior information to generate synthetic poses and use the synthesized poses to train our model. In the testing stage, pose estimation results of any other methods can be input to the proposed method. Moreover, the proposed model does not require code or knowledge about other methods, which allows it to be easily used in the post-processing step. We show that the proposed approach achieves better performance than the conventional multi-stage refinement models and consistently improves the performance of various state-of-the-art pose estimation methods on the commonly used benchmark. The code is available in (https://github. com/mks0601/PoseFix_RELEASE).

Hands are often severely occluded by objects, which makes 3D hand mesh estimation challenging. Previous works often have disregarded information at occluded regions. However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand mesh estimation. Thus, in this work, we propose a novel 3D hand mesh estimation network HandOccNet, that can fully exploits the information at occluded regions as a secondary means to enhance image features and make it much richer. To this end, we design two successive Transformer-based modules, called feature injecting transformer (FIT) and self-enhancing transformer (SET). FIT injects hand information into occluded region by considering their correlation. SET refines the output of FIT by using a self-attention mechanism. By injecting the hand information to the occluded region, our HandOccNet reaches the state-of-the-art performance on 3D hand mesh benchmarks that contain challenging hand-object occlusions. The codes are available in: https://github. com/namepllet/HandOccNet.

Whole-body 3D human mesh estimation aims to reconstruct the 3D human body, hands, and face simultaneously. Although several methods have been proposed, accurate prediction of 3D hands, which consist of 3D wrist and fingers, still remains challenging due to two reasons. First, the human kinematic chain has not been carefully considered when predicting the 3D wrists. Second, previous works utilize body features for the 3D fingers, where the body feature barely contains finger information. To resolve the limitations, we present Hand4Whole, which has two strong points over previous works. First, we design Pose2Pose, a module that utilizes joint features for 3D joint rotations. Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain. Second, Hand4Whole discards the body feature when predicting 3D finger rotations. Our Hand4Whole is trained in an end-to-end manner and produces much better 3D hand results than previous whole-body 3D human mesh estimation methods. The codes are available here (https://github. com/mks0601/Hand4Whole_RELEASE).

We consider the problem of recovering a single person's 3D human mesh from in-the-wild crowded scenes. While much progress has been in 3D human mesh estimation, existing methods struggle when test input has crowded scenes. The first reason for the failure is a domain gap between training and testing data. A motion capture dataset, which provides accurate 3D labels for training, lacks crowd data and impedes a network from learning crowded scene-robust image features of a target person. The second reason is a feature processing that spatially averages the feature map of a localized bounding box containing multiple people. Averaging the whole feature map makes a target person's feature indistinguishable from others. We present 3DCrowdNet that firstly explicitly targets in-the-wild crowded scenes and estimates a robust 3D human mesh by addressing the above issues. First, we leverage 2D human pose estimation that does not require a motion capture dataset with 3D labels for training and does not suffer from the domain gap. Second, we propose a joint-based regressor that distinguishes a target person's feature from others. Our joint-based regressor preserves the spatial activation of a target by sampling features from the target's joint locations and regresses human model parameters. As a result, 3DCrowdNet learns target-focused features and effectively excludes the irrelevant features of nearby persons. We conduct experiments on various benchmarks and prove the robustness of 3DCrowdNet to the in-the-wild crowded scenes both quantitatively and qualitatively. Codes are available here: https://github. com/hongsukchoi …

Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human model parameters and weakly supervised with GT 2D/3D joint coordinates as the 3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D joint coordinates of training sets in the pre-processing stage of the regressors. The fitted 3D parameters at the last fitting iteration become the 3D pseudo-GTs, used to fully supervise the regressors. Optimization-based annotators, such as SMPLify-X, have been widely used to obtain the 3D pseudo-GTs. However, they often produce wrong 3D pseudo-GTs as they fit the 3D parameters to GT of each sample independently. To overcome the limitation, we present NeuralAnnot, a neural network-based annotator. The main idea of NeuralAnnot is to employ a neural network-based regressor and dedicate it for the annotation. Assuming no 3D pseudo-GTs are available, NeuralAnnot is weakly supervised with GT 2D/3D joint coordinates of training sets. The testing results on the same training sets become 3D pseudo-GTs, used to fully supervise the regressors. We show that 3D pseudo-GTs of NeuralAnnot are highly beneficial to train the regressors. We made our 3D pseudo-GTs publicly available.

Facial expression and hand motions are necessary to express our emotions and interact with the world. Nevertheless, most of the 3D human avatars modeled from a casually captured video only support body motions without facial expressions and hand motions. In this work, we present ExAvatar , an expressive whole-body 3D human avatar learned from a short monocular video. We design ExAvatar as a combination of the whole-body parametric mesh model (SMPL-X) and 3D Gaussian Splatting (3DGS). The main challenges are 1) a limited diversity of facial expressions and poses in the video and 2) the absence of 3D observations, such as 3D scans and RGBD images. The limited diversity in the video makes animations with novel facial expressions and poses non-trivial. In addition, the absence of 3D observations could cause significant ambiguity in human parts that are not observed in the video, which can …

Human hands play a central role in interacting with other people and objects. For realistic replication of such hand motions, high-fidelity hand meshes have to be reconstructed. In this study, we firstly propose DeepHandMesh, a weakly-supervised deep encoder-decoder framework for high-fidelity hand mesh modeling. We design our system to be trained in an end-to-end and weakly-supervised manner; therefore, it does not require groundtruth meshes. Instead, it relies on weaker supervisions such as 3D joint coordinates and multi-view depth maps, which are easier to get than groundtruth meshes and do not dependent on the mesh topology. Although the proposed DeepHandMesh is trained in a weakly-supervised way, it provides significantly more realistic hand mesh than previous fully-supervised hand models. Our newly introduced penetration avoidance loss further improves results by replicating physical …

Although much progress has been made in 3D clothed human reconstruction, most of the existing methods fail to produce robust results from in-the-wild images, which contain diverse human poses and appearances. This is mainly due to the large domain gap between training datasets and in-the-wild datasets. The training datasets are usually synthetic ones, which contain rendered images from GT 3D scans. However, such datasets contain simple human poses and less natural image appearances compared to those of real in-the-wild datasets, which makes generalization of it to in-the-wild images extremely challenging. To resolve this issue, in this work, we propose ClothWild, a 3D clothed human reconstruction framework that firstly addresses the robustness on in-the-wild images. First, for the robustness to the domain gap, we propose a weakly supervised pipeline that is trainable with 2D supervision targets of …

We tackle the problem of generating long-term 3D human motion from multiple action labels. Two main previous approaches, such as action-and motion-conditioned methods, have limitations to solve this problem. The action-conditioned methods generate a sequence of motion from a single action. Hence, it cannot generate long-term motions composed of multiple actions and transitions between actions. Meanwhile, the motion-conditioned methods generate future motions from initial motion. The generated future motions only depend on the past, so they are not controllable by the user's desired actions. We present MultiAct, the first framework to generate long-term 3D human motion from multiple action labels. MultiAct takes account of both action and motion conditions with a unified recurrent generation system. It repetitively takes the previous motion and action label; then, it generates a smooth transition and the motion of the given action. As a result, MultiAct produces realistic long-term motion controlled by the given sequence of multiple action labels. The code is publicly available in https://github. com/TaeryungLee/MultiAct RELEASE.

Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input. While being effective in exploiting contextual information around humans, eg, human appearance and scene category, they are easily fooled by out-of-context action videos where the contexts do not exactly match with target actions. In contrast, pose-based methods, which take a sequence of human skeletons only as input, suffer from inaccurate pose estimation or ambiguity of human pose per se. Integrating these two approaches has turned out to be non-trivial; training a model with both appearance and pose ends up with a strong bias towards appearance and does not generalize well to unseen videos. To address this problem, we propose to learn pose-driven feature integration that dynamically combines appearance and pose streams by observing pose features on the fly. The main idea is to let the pose stream decide how much and which appearance information is used in integration based on whether the given pose information is reliable or not. We show that the proposed IntegralAction achieves highly robust performance across in-context and out-of-context action video datasets. The codes are available in https://github. com/mks0601/IntegralAction_RELEASE.

The two-hand interaction is one of the most challenging signals to analyze due to the self-similarity, complicated articulations, and occlusions of hands. Although several datasets have been proposed for the two-hand interaction analysis, all of them do not achieve 1) diverse and realistic image appearances and 2) diverse and large-scale groundtruth (GT) 3D poses at the same time. In this work, we propose Re: InterHand, a dataset of relighted 3D interacting hands that achieve the two goals. To this end, we employ a state-of-the-art hand relighting network with our accurately tracked two-hand 3D poses. We compare our Re: InterHand with existing 3D interacting hands datasets and show the benefit of it. Our Re: InterHand is available in https://mks0601. github. io/ReInterHand/

This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in low-resolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution.

VCAI Lab

교수님 소개

문경식

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image

Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator

PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

문경식

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image

Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator

PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집