CVPR 2024 Papers

We propose a single-shot approach to determining 6-DoF pose of an object with available 3D computer-aided design (CAD) model from a single RGB image. Our method dubbed MRC-Net comprises two stages. The first performs pose classification and renders the 3D object in the classified pose. The second stage performs regression to predict fine-grained residual pose within class. Connecting the two stages is a novel multi-scale residual correlation (MRC) layer that captures high-and-low level correspondences between the input image and rendering from first stage. MRC-Net employs a Siamese network with shared weights between both stages to learn embeddings for input and rendered images. To mitigate ambiguity when predicting discrete pose class labels on symmetric objects we use soft probabilistic labels to define pose class in the first stage. We demonstrate state-of-the-art accuracy outperforming all competing RGB-based methods on four challenging BOP benchmark datasets: T-LESS LM-O YCB-V and ITODD. Our method is non-iterative and requires no complex post-processing. Our code and pretrained models are available at https://github.com/amzn/mrc-net-6d-pose

Foot contact is an important cue for human motion capture understanding and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion as well as accurate and dense foot-contact annotation. To fill this gap we propose a Multimodal MoCap Dataset with Vision and Pressure sensors named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations which is especially useful for both plausible shape estimation robust pose fitting without foot drifting and accurate global translation tracking. To validate the dataset we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework VP-MoCap for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://metaverse-ai-lab-thu.github.io/MMVP-Dataset/.

Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless numerous limitations exist within existing public MSMO datasets including insufficient maintenance data inaccessibility limited size and the absence of proper categorization which pose significant challenges. To address these challenges and provide a comprehensive dataset for this new direction we have meticulously curated the MMSum dataset. Our new dataset features (1) Human-validated summaries for both video and textual content providing superior human instruction and labels for multimodal learning. (2) Comprehensively and meticulously arranged categorization spanning 17 principal categories and 170 subcategories to encapsulate a diverse array of real-world scenarios. (3) Benchmark tests performed on the proposed dataset to assess various tasks and methods including video summarization text summarization and multimodal summarization. To champion accessibility and collaboration we released the MMSum dataset and the data collection tool as fully open-source resources fostering transparency and accelerating future developments.

We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams quizzes and textbooks covering six core disciplines: Art & Design Business Science Health & Medicine Humanities & Social Science and Tech & Engineering. These questions span 30 subjects and 183 subfields comprising 30 highly heterogeneous image types such as charts diagrams maps tables music sheets and chemical structures. Unlike existing benchmarks MMMU focuses on advanced perception and reasoning with domain-specific knowledge challenging models to perform tasks akin to those faced by experts. The evaluation of 28 open-source LMMs as well as the proprietary GPT-4V(ision) and Gemini highlights the substantial challenges posed by MMMU. Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively indicating significant room for improvement. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence.

We propose Multiscale Correlation searching homography estimation Network namely MCNet an iterative deep homography estimation architecture. Different from previous approaches that achieve iterative refinement by correlation searching within a single scale MCNet combines the multiscale strategy with correlation searching incurring nearly ignored computational overhead. Moreover MCNet adopts a Fine-Grained Optimization loss function named FGO loss to further boost the network training at the convergent stage which can improve the estimation accuracy without additional computational overhead. According to our experiments using the above two simple strategies can produce significant homography estimation accuracy with considerable efficiency. We show that MCNet achieves state-of-the-art performance on a variety of datasets including common scene MSCOCO cross-modal scene GoogleEarth and GoogleMap and dynamic scene SPID. Compared to the previous SOTA method 2-scale RHWF our MCNet reduces inference time FLOPs parameter cost and memory cost by 78.9% 73.5% 34.1% and 33.2% respectively while achieving 20.5% (MSCOCO) 43.4% (GoogleEarth) and 41.1% (GoogleMap) mean average corner error (MACE) reduction. Source code is available at https://github.com/zjuzhk/MCNet.

Perception plays a crucial role in various robot applications. However existing well-annotated datasets are biased towards autonomous driving scenarios while unlabelled SLAM datasets are quickly over-fitted and often lack environment and domain variations. To expand the frontier of these fields we introduce a comprehensive dataset named MCD (Multi-Campus Dataset) featuring a wide range of sensing modalities high-accuracy ground truth and diverse challenging environments across three Eurasian university campuses. MCD comprises both CCS (Classical Cylindrical Spinning) and NRE (Non-Repetitive Epicyclic) lidars high-quality IMUs (Inertial Measurement Units) cameras and UWB (Ultra-WideBand) sensors. Furthermore in a pioneering effort we introduce semantic annotations of 29 classes over 59k sparse NRE lidar scans across three domains thus providing a novel challenge to existing semantic segmentation research upon this largely unexplored lidar modality. Finally we propose for the first time to the best of our knowledge continuous-time ground truth based on optimization-based registration of lidar-inertial data on large survey-grade prior maps which are also publicly released each several times the size of existing ones. We conduct a rigorous evaluation of numerous state-of-the-art algorithms on MCD report their performance and highlight the challenges awaiting solutions from the research community.

Limited training data is a long-standing problem for video emotion analysis (VEA). Existing works leverage the power of large-scale image datasets for transferring while failing to extract the temporal correlation of affective cues in the video. Inspired by psychology research and empirical theory we verify that the degree of emotion may vary in different segments of the video thus introducing the sentiment complementary and emotion intrinsic among temporal segments. We propose an MAE-style method for learning robust affective representation of videos via masking termed MART. First we extract the affective cues of the lexicon and verify the extracted one by computing its matching score with video content in terms of sentiment and emotion scores alongside the temporal dimension. Then with the verified cues we propose masked affective modeling to recover temporal emotion distribution. We present temporal affective complementary learning that pulls the complementary part and pushes the intrinsic one of masked multimodal features where the constraint is set with cross-modal attention among features to mask the video and recover the degree of emotion among segments. Extensive experiments on five benchmarks show the superiority of our method in video sentiment analysis video emotion recognition multimodal sentiment analysis and multimodal emotion recognition.

Understanding how we grasp objects with our hands has important applications in areas like robotics and mixed reality. However this challenging problem requires accurate modeling of the contact between hands and objects.To capture grasps existing methods use skeletons meshes or parametric models that does not represent hand shape accurately resulting in inaccurate contacts. We present MANUS a method for Markerless Hand-Object Grasp Capture using Articulated 3D Gaussians. We build a novel articulated 3D Gaussians representation that extends 3D Gaussian splatting for high-fidelity representation of articulating hands. Since our representation uses Gaussian primitives optimized from the multi-view pixel-aligned losses it enables us to efficiently and accurately estimate contacts between the hand and the object. For the most accurate results our method requires tens of camera views that current datasets do not provide. We therefore build MANUS-Grasps a new dataset that contains hand-object grasps viewed from 50+ cameras across 30+ scenes 3 subjects and comprising over 7M frames. In addition to extensive qualitative results we also show that our method outperforms others on a quantitative contact evaluation method that uses paint transfer from the object to the hand.

We introduce MAGICK a large-scale dataset of generated objects with high-quality alpha mattes. While image generation methods have produced segmentations they cannot generate alpha mattes with accurate details in hair fur and transparencies. This is likely due to the small size of current alpha matting datasets and the difficulty in obtaining ground-truth alpha. We propose a scalable method for synthesizing images of objects with high-quality alpha that can be used as a ground-truth dataset. A key idea is to generate objects on a single-colored background so chroma keying approaches can be used to extract the alpha. However this faces several challenges including that current text-to-image generation methods cannot create images that can be easily chroma keyed and that chroma keying is an underconstrained problem that generally requires manual intervention for high-quality results. We address this using a combination of generation and alpha extraction methods. Using our method we generate a dataset of 150000 objects with alpha. We show the utility of our dataset by training an alpha-to-rgb generation method that outperforms baselines. Please see our project website at https://ryanndagreat.github.io/MAGICK/.

The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal congenital heart disease. In practice there is a large domain gap between different hospitals' data such as the variable data quality due to differences in acquisition equipment. In addition accurate annotation information provided by obstetrician experts is always very costly or even unavailable. This study explores the unsupervised domain adaptive fetal cardiac structure detection issue. Existing unsupervised domain adaptive object detection (UDAOD) approaches mainly focus on detecting objects in natural scenes such as Foggy Cityscapes where the structural relationships of natural scenes are uncertain. Unlike all previous UDAOD scenarios we first collected a Fetal Cardiac Structure dataset from two hospital centers called FCS and proposed a multi-matching UDA approach (M3-UDA) including Histogram Matching (HM) Sub-structure Matching (SM) and Global-structure Matching (GM) to better transfer the topological knowledge of anatomical structure for UDA detection in medical scenarios. HM mitigates the domain gap between the source and target caused by pixel transformation. SM fuses the different angle information of the sub-structure to obtain the local topological knowledge for bridging the domain gap of the internal sub-structure. GM is designed to align the global topological knowledge of the whole organ from the source and target domain. Extensive experiments on our collected FCS and CardiacUDA and experimental results show that M3-UDA outperforms existing UDAOD studies significantly. All datasets and source code are available at : https://github.com/xmed-lab/M3-UDA

Existing wearable motion capture methods typically demand tight on-body fixation (often using straps) for reliable sensing limiting their application in everyday life. In this paper we introduce Loose Inertial Poser a novel motion capture solution with high wearing comfortableness by integrating four Inertial Measurement Units (IMUs) into a loose-wear jacket. Specifically we address the challenge of scarce loose-wear IMU training data by proposing a Secondary Motion AutoEncoder (SeMo-AE) that learns to model and synthesize the effects of secondary motion between the skin and loose clothing on IMU data. SeMo-AE is leveraged to generate a diverse synthetic dataset of loose-wear IMU data to augment training for the pose estimation network and significantly improve its accuracy. For validation we collected a dataset with various subjects and 2 wearing styles (zipped and unzipped). Experimental results demonstrate that our approach maintains high-quality real-time posture estimation even in loose-wear scenarios.

Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM) yet face challenges such as error accumulation during camera tracking resulting in distorted maps. In response we introduce Loopy-SLAM that globally optimizes poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Robust pose graph optimization is used to rigidly align the local submaps. As our representation is point based map corrections can be performed efficiently without the need to store the entire history of input frames used for mapping as typically required by methods employing a grid based mapping structure. Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet datasets demonstrate competitive or superior performance in tracking mapping and rendering accuracy when compared to existing dense neural RGBD SLAM methods. Project page: notchla.github.io/Loopy-SLAM.

Long-tail class incremental learning (LT-CIL) is designed to perpetually acquire novel knowledge from an imbalanced and perpetually evolving data stream while ensuring the retention of previously acquired knowledge. The existing method only re-balances data distribution and ignores exploring the potential relationship between different samples causing non-robust representations and even severe forgetting in classes with few samples. In this paper we constructed two parallel spaces simultaneously: 1) Sub-prototype space and 2) Reminiscence space to learn robust representations while alleviating forgetfulness. Concretely we advance the concept of the sub-prototype space which amalgamates insights from diverse classes. This integration facilitates the mutual complementarity of varied knowledge thereby augmenting the attainment of more robust representations. Furthermore we introduce the reminiscence space which encapsulates each class distribution aiming to constraint model optimization and mitigate the phenomenon of forgetting. The tandem utilization of the two parallel spaces effectively alleviates the adverse consequences associated with imbalanced data distribution preventing forgetting without needing replay examples. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various benchmarks.

The task of online mapping is to predict a local map using current sensor observations e.g. from lidar and camera without relying on a pre-built map. State-of-the-art methods are based on supervised learning and are trained predominantly using two datasets: nuScenes and Argoverse 2. However these datasets revisit the same geographic locations across training validation and test sets. Specifically over 80% of nuScenes and 40% of Argoverse 2 validation and test samples are less than 5 m from a training sample. At test time the methods are thus evaluated more on how well they localize within a memorized implicit map built from the training data than on extrapolating to unseen locations. Naturally this data leakage causes inflated performance numbers and we propose geographically disjoint data splits to reveal the true performance in unseen environments. Experimental results show that methods perform considerably worse some dropping more than 45 mAP when trained and evaluated on proper data splits. Additionally a reassessment of prior design choices reveals diverging conclusions from those based on the original split. Notably the impact of lifting methods and the support from auxiliary tasks (e.g. depth supervision) on performance appears less substantial or follows a different trajectory than previously perceived.

Estimating disparities in challenging areas is difficult and limits the performance of stereo matching models. In this paper we exploit local structure information (LSI) to enhance stereo matching. Specifically our LSI comprises a series of key elements including the slant plane (parameterised by disparity gradients) disparity offset details and neighbouring relations. This LSI empowers our method to effectively handle intricate structures including object boundaries and curved surfaces. We bootstrap the LSI from monocular depth and subsequently iteratively refine it to better capture the underlying scene geometry constraints. Building upon the LSI we introduce the Local Structure-Guided Propagation (LSGP) which enhances the disparity initialization optimization and refinement processes. By combining LSGP with a Gated Recurrent Unit (GRU) we present our novel stereo matching method referred to as Local Structure-guided stereo matching (LoS). Remarkably LoS achieves top-ranking results on four widely recognized public benchmark datasets (ETH3D Middlebury KITTI 15 & 12) demonstrating the superior capabilities of our proposed model.

For human-centric large-scale scenes fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications. In this paper we present LiveHPS a novel single-LiDAR-based approach for scene-level human pose and shape estimation without any limitation of light conditions and wearable devices. In particular we design a distillation mechanism to mitigate the distribution-varying effect of LiDAR point clouds and exploit the temporal-spatial geometric and dynamic information existing in consecutive frames to solve the occlusion and noise disturbance. LiveHPS with its efficient configuration and high-quality output is well-suited for real-world applications. Moreover we propose a huge human motion dataset named FreeMotion which is collected in various scenarios with diverse human poses shapes and translations. It consists of multi-modal and multi-view acquisition data from calibrated and synchronized LiDARs cameras and IMUs. Extensive experiments on our new dataset and other public datasets demonstrate the SOTA performance and robustness of our approach. We will release our code and dataset soon.

In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3.6 billion precisely point-level annotated points covering an expansive area of 30000m^2. It encompasses three prevalent daily environments including learning scenes working scenes and living scenes. LiDAR-Net is characterized by its non-uniform point distribution e.g. scanning holes and scanning lines. Additionally it meticulously records and annotates scanning anomalies including reflection noise and ghost. These anomalies stem from specular reflections on glass or metal as well as distortions due to moving persons. LiDAR-Net's realistic representation of non-uniform distribution and anomalies significantly enhances the training of deep learning models leading to improved generalization in practical applications. We thoroughly evaluate the performance of state-of-the-art algorithms on LiDAR-Net and provide a detailed analysis of the results. Crucially our research identifies several fundamental challenges in understanding indoor point clouds contributing essential insights to future explorations in this field. Our dataset can be found online: http://lidar-net.njumeta.com

In Structure-from-Motion (SfM) the underlying viewgraphs of unordered image collections generally have a highly redundant set of edges that can be sparsified for efficiency without significant loss of reconstruction quality. Often there are also false edges due to incorrect image retrieval and repeated structures (symmetries) that give rise to ghosting and superimposed reconstruction artifacts. We present a unified method to simultaneously sparsify the viewgraph and remove false edges. We propose a scoring mechanism based on camera triplets that identifies edge redundancy as well as false edges. Our edge selection is formulated as an optimization problem which can be provably solved using a simple thresholding scheme. This results in a highly efficient algorithm which can be incorporated as a pre-processing step into any SfM pipeline making it practically usable. We demonstrate the utility of our method on generic and ambiguous datasets that cover the range of small medium and large-scale datasets all with different statistical properties. Sparsification of generic datasets using our method significantly reduces reconstruction time while maintaining the accuracy of the reconstructions as well as removing ghosting artifacts. For ambiguous datasets our method removes false edges thereby avoiding incorrect superimposed reconstructions.

Egocentric videos provide a first-person perspective of the wearer's activities involving simultaneous interactions with multiple objects. In this work we propose the task of weakly-supervised Narration-based Video Object Segmentation (NVOS). Given an egocentric video clip and a narration of the wearer's activities our aim is to segment object instances mentioned in the narration without using any spatial annotations during training. Existing weakly-supervised video object grounding methods typically yield bounding boxes for referred objects. In contrast we propose ROSA a weakly-supervised pixel-level grounding framework learning alignments between referred objects and segmentation mask proposals. Our model harnesses vision-language models pre-trained on image-text pairs to embed region masks and object phrases. During training we combine (a) a video-narration contrastive loss that implicitly supervises the alignment between regions and phrases and (b) a region-phrase contrastive loss based on inferred latent alignments. To address the lack of annotated NVOS datasets in egocentric videos we create a new evaluation benchmark VISOR-NVOS leveraging existing annotations of segmentation masks from VISOR alongside 14.6k newly-collected object-based video clip narrations. Our approach achieves state-of-the-art zero-shot pixel-level grounding performance compared to strong baselines under similar supervision. Additionally we demonstrate generalization capabilities for zero-shot video object grounding on YouCook2 a third-person instructional video dataset.