Learning 3D human motion from 2D inputs is a fundamental task in the realms of computer vision and computer graphics. Many previous methods grapple with this inherently ambiguous task by introducing motion priors into the learning process. However, these approaches face difficulties in defining the complete configurations of such priors or training a robust model. In this paper, we present the Video-to-Motion Generator (VTM), which leverages motion priors through cross-modal latent feature space alignment between 3D human motion and 2D inputs, namely videos and 2D keypoints. To reduce the complexity of modeling motion priors, we model the motion data separately for the upper and lower body parts. Additionally, we align the motion data with a scale-invariant virtual skeleton to mitigate the interference of human skeleton variations to the motion priors. Evaluated on AIST++, the VTM showcases state-of-the-art performance in reconstructing 3D human motion from monocular videos. Notably, our VTM exhibits the capabilities for generalization to unseen view angles and in-the-wild videos.
Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods. However, the existing methods usually apply non-end-to-end training strategies and insufficiently leverage the LiDAR information, where the rich potential of the LiDAR data has not been well exploited. In this paper, we propose the Cross-Modality Knowledge Distillation (CMKD) network for monocular 3D detection to efficiently and directly transfer the knowledge from LiDAR modality to image modality on both features and responses. Moreover, we…
Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；École Polytechnique（巴黎综合理工学院）；Technical University of Munich（慕尼黑工业大学）
Learning-based depth estimation has witnessed recent progress in multiple directions; from self-supervision using monocular video to supervised methods offering highest accuracy. Complementary to supervision, further boosts to performance and robustness are gained by combining information from multiple signals. In this paper we systematically investigate key trade-offs associated with sensor and modality design choices as well as related model training strategies. Our study leads us to a new method, capable of connecting modality-specific advantages from polarisation, Time-of-Flight and struct…
Nagoya University（名古屋大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）
This paper proposes a novel method of estimating the absolute scale of monocular SfM for a multi-modal stereo camera. In the fields of computer vision and robotics, scale estimation for monocular SfM has been widely investigated in order to simplify systems. This paper addresses the scale estimation problem for a stereo camera system in which two cameras capture different spectral images (e.g., RGB and FIR), whose feature points are difficult to directly match using descriptors. Furthermore, the number of matching points between FIR images can be comparatively small, owing to the low resolutio…
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海市智能视觉与影像工程研究中心）
Capturing challenging human motions is critical for numerous applications, but it suffers from complex motion patterns and severe self-occlusion under the monocular setting. In this paper, we propose ChallenCap -- a template-based approach to capture challenging 3D human motions using a single RGB camera in a novel learning-and-optimization framework, with the aid of multi-modal references. We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion di…
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）；School of Micro-Nano Electronics, Zhejiang University（浙江大学微纳电子学院）
In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we propose a two-step feature alignment approach. In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores. In the second step, the center alignment is used to align the features at 2D/3D centers. Further, it is often difficult to learn global …
SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学学院）；Basic Algorithm Center, PCG, Tencent（腾讯计算机系统有限公司基础算法中心）
We present a visual-inertial depth estimation pipeline that integrates monocular depth estimation and visual-inertial odometry to produce dense depth estimates with metric scale. Our approach performs global scale and shift alignment against sparse metric depth, followed by learning-based dense alignment. We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in inverse RMSE with dense scale alignment relative to performing just global alignment alone. Our approach is especially competitive at low density; with just 150 sparse metric depth points, our dense-to-dense dept…
Depth AnyEvent: A Cross-Modal Distillation Paradigm for Event-Based Monocular Depth Estimation
Advanced Research Center on Electronic System (ARCES)（电子系统先进研究中心）；Department of Computer Science and Engineering (DISI)（计算机科学与工程系）；University of Bologna（博洛尼亚大学）
Robust 3D Alignment of Generative Reconstructions via Partial Monocular Observations
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；School of Tech Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Independent Researcher（独立研究员）；School of Electronic Engineering and Information Science, University of Science and Technology of China（中国科学技术大学电子工程与信息科学学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Robotics, Wuhan University（武汉大学机器人学院）；Xiaomi EV（小米汽车）
BNRist（北京国家信息科学与技术研究中心）；School of Software, Tsinghua University（清华大学软件学院）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）
We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new learning based architecture designed such that it can make use of all the sources of available hand training data: image data with either 2D or 3D annotations, as well as stand-alone 3D animations without corresponding image data. It features a 3D hand joint detection module and an inverse kinematics module which regresses not only 3D joint positions but also maps them to joint rotations in a single feed-forward pass. T…
SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimation
Technical University of Munich（慕尼黑技术大学）；BMW Group（宝马集团）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；Google（谷歌）；VisualAIs Labs GmbH（VisualAIs实验室 GmbH）
LISMARS；Hubei Luojia Laboratory（湖北珞珈实验室）；Wuhan University（武汉大学）；The University of Hong Kong（香港大学）；The Hong Kong Polytechnic University（香港理工大学）；Texas A&M University（德克萨斯农工大学）
Beijing University of Posts and Telecommunications（北京邮电大学）
The performance of monocular depth estimation generally depends on the amount of parameters and computational cost. It leads to a large accuracy contrast between light-weight networks and heavy-weight networks, which limits their application in the real world. In this paper, we model the majority of accuracy contrast between them as the difference of depth distribution, which we call "Distribution drift". To this end, a distribution alignment network (DANet) is proposed. We firstly design a pyramid scene transformer (PST) module to capture inter-region interaction in multiple scales. By percei…
Nanjing University of Science and Technology（南京理工大学）
In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as panoramic 3D cameras often produce 360° depth with missing data in complex scenes. Its goal is to recover dense panoramic depths from raw sparse ones and panoramic RGB images. To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery. However, it needs to face a challenging optimization problem of the network parameters due to its non-convex objective function. To address this problem, we propose a simple yet effective approach te…
Bidirectional Regression for Monocular 6DoF Head Pose Estimation and Reference System Alignment
Department of Electronics and Communications Engineering, Kwangwoon University（韩国 Kwangwoon 大学电子与通信工程系）；Department of Software Engineering, Dankook University（韩国 Dankook 大学软件工程系）；School of Computer Science, University of Birmingham（英国 Birmingham 大学计算机科学学院）
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；EPFL（洛桑联邦理工学院）
We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead t…
Moving6DPoSe: A Multimodal Database for Monocular 6D Pose Estimation and Segmentation of Moving Objects
University of Chile（智利大学）；Advanced Mining Technology Center (AMTC), University of Chile（智利大学先进采矿技术中心）；The Iniciativa de Datos e Inteligencia Artificial, University of Chile（智利大学数据与人工智能计划）
Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUs
Department of Electrical, Electronic, and Information Engineering (DEI), University of Bologna（电气电子与信息工程系，博洛尼亚大学）；Department of Control and Computer Engineering (DAUIN), Politecnico di Torino（控制与计算机工程系，托斯卡纳理工学院）；Department of Electrical Engineering (ESAT) at KU Leuven（根特大学电子工程系）；Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI（达勒莫人工智能研究所（IDSIA），USI-SUPSI）；Department of Information Technology and Electrical Engineering (D-ITET), ETH Zurich（信息科技与电气工程系，苏黎世联邦理工学院）；Integrated Systems Laboratory, ETH Zurich（集成系统实验室，苏黎世联邦理工学院）
VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency
National Key Laboratory of Science and Technology on Multi-spectral Information Processing（科学与技术多光谱信息处理国家重点实验室）；Huazhong University of Science and Technology（华中科技大学）；College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）
PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images
Department of Automation, Tsinghua University（自动化系，清华大学）；Department of Computer Science and Technology, Nanjing University（计算机科学与技术系，南京大学）；Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）
Text-based person retrieval aims to find the query person based on a textual description. The key is to learn a common latent space mapping between visual-textual modalities. To achieve this goal, existing works employ segmentation to obtain explicitly cross-modal alignments or utilize attention to explore salient alignments. These methods have two shortcomings: 1) Labeling cross-modal alignments are time-consuming. 2) Attention methods can explore salient cross-modal alignments but may ignore some subtle and valuable pairs. To relieve these issues, we introduce an Implicit Visual-Textual (IVT…
Georgia Institute of Technology（佐治亚理工学院）；Wuhan University of Technology（武汉理工大学）
This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images. The first tier improves recipe text embedding by optimizing the LSTM networks with term extraction and ranking enhanced sequence patterns, and optimizes the image embedding by combining the ResNeXt-101 image encoder with the category embedding using wideResNet-50 with word2vec. The second tier modality alignment optimizes the textual-visual joint embedding loss function using a double batch-hard triplet loss with soft…
Technical University of Munich（慕尼黑工业大学）；Tsinghua University（清华大学）；Google（谷歌）
6D object pose estimation is a fundamental problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliable 6D pose estimates even from monocular images. Nonetheless, CNNs are identified as being extremely data-driven, and acquiring adequate annotations is oftentimes very time-consuming and labor intensive. To overcome this shortcoming, we propose the idea of monocular 6D pose estimation by means of self-supervised learning, removing the need for real annotations. After training our proposed network fully supervised with synthetic RGB d…
360° cameras can capture complete environments in a single shot, which makes 360° imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360° data, particularly for high resolutions like 2K (2048x1024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose a flexible framework for monocular depth estimation from high-resolution 360° images using tangent images. We project the 360° input image on…
AlignFreeNet: Is Cross-Modal Pre-Alignment Necessary? An End-to-End Alignment-Free Lightweight Network for Visible-Infrared Object Detection
School of Computer Science, Jiangsu University of Technology（江苏科技大学计算机科学学院）；School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科技学院）；School of Data Science, Lingnan University（岭南大学数据科学学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
