In this era the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However in the realm of 3D vision while remarkable progress has been made with models trained on large-scale synthetic and real-captured object data like Objaverse and MVImgNet a similar level of progress has not been observed in the domain of human-centric tasks partially due to the lack of a large-scale human dataset. Existing datasets of high-fidelity 3D human capture continue to be mid-sized due to the significant challenges in acquiring large-scale high-quality 3D human data. To bridge this gap we present MVHumanNet a dataset that comprises multi-view human action sequences of 4500 human identities. The primary focus of our work is on collecting human data that features a large number of diverse identities and everyday clothing using a multi-view human capture system which facilitates easily scalable data collection. Our dataset contains 9000 daily outfits 60000 motion sequences and 645 million frames with extensive annotations including human masks camera parameters 2D and 3D keypoints SMPL/SMPLX parameters and corresponding textual descriptions. To explore the potential of MVHumanNet in various 2D and 3D visual tasks we conducted pilot studies on view-consistent action recognition human NeRF reconstruction text-driven view-unconstrained human image generation as well as 2D view-unconstrained human image and 3D avatar generation. Extensive experiments demonstrate the performance improvements and effective applications enabled by the scale provided by MVHumanNet. As the current largest-scale 3D human dataset we hope that the release of MVHumanNet data with annotations will foster further innovations in the domain of 3D human-centric tasks at scale.
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Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling
PDF ↗Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of research data collection it would be appealing to autonomously explore patterns and insights from observational data for discovering novel classes of phenotypes and concepts. However in the biomedical domain there are several challenges inherently presented in the cumulated data which hamper the progress of novel class discovery. The non-i.i.d. data distribution accompanied by the severe imbalance among different groups of classes essentially leads to ambiguous and biased semantic representations. In this work we present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. First we propose to parameterize the approximated posterior of instance embedding as a marginal von Mises-Fisher distribution to account for the interference of distributional latent bias. Then we incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space which in turn minimizes the uncontrollable risk for unknown class learning and structuring. Furthermore a spectral graph-theoretic method is devised to estimate the number of potential novel classes. It inherits two intriguing merits compared to existent approaches namely high computational efficiency and flexibility for taxonomy-adaptive estimation. Extensive experiments across various biomedical scenarios substantiate the effectiveness and general applicability of our method.
The pretraining-finetuning paradigm has gained popularity in various computer vision tasks. In this paradigm the emergence of active finetuning arises due to the abundance of large-scale data and costly annotation requirements. Active finetuning involves selecting a subset of data from an unlabeled pool for annotation facilitating subsequent finetuning. However the use of a limited number of training samples can lead to a biased distribution potentially resulting in model overfitting. In this paper we propose a new method called ActiveDC for the active finetuning tasks. Firstly we select samples for annotation by optimizing the distribution similarity between the subset to be selected and the entire unlabeled pool in continuous space. Secondly we calibrate the distribution of the selected samples by exploiting implicit category information in the unlabeled pool. The feature visualization provides an intuitive sense of the effectiveness of our approach to distribution calibration. We conducted extensive experiments on three image classification datasets with different sampling ratios. The results indicate that ActiveDC consistently outperforms the baseline performance in all image classification tasks. The improvement is particularly significant when the sampling ratio is low with performance gains of up to 10%. Our code will be released.
We develop a theory for the representation of opaque solids as volumes. Starting from a stochastic representation of opaque solids as random indicator functions we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the probability distributions of the underlying indicator functions. We generalize our theory to account for isotropic and anisotropic scattering at different parts of the solid and for representations of opaque solids as stochastic implicit surfaces. We derive our volumetric representation from first principles which ensures that it satisfies physical constraints such as reciprocity and reversibility. We use our theory to explain compare and correct previous volumetric representations as well as propose meaningful extensions that lead to improved performance in 3D reconstruction tasks.
When adopting a deep learning model for embodied agents it is required that the model structure be optimized for specific tasks and operational conditions. Such optimization can be static such as model compression or dynamic such as adaptive inference. Yet these techniques have not been fully investigated for embodied control systems subject to time constraints which necessitate sequential decision-making for multiple tasks each with distinct inference latency limitations. In this paper we present MoDeC a time constraint-aware embodied control framework using the modular model adaptation. We formulate model adaptation to varying operational conditions on resource and time restrictions as dynamic routing on a modular network incorporating these conditions as part of multi-task objectives. Our evaluation across several vision-based embodied environments demonstrates the robustness of MoDeC showing that it outperforms other model adaptation methods in both performance and adherence to time constraints in robotic manipulation and autonomous driving applications.
Video and audio content creation serves as the core technique for the movie industry and professional users. Recently existing diffusion-based methods tackle video and audio generation separately which hinders the technique transfer from academia to industry. In this work we aim at filling the gap with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation. We observe the powerful generation ability of off-the-shelf video or audio generation models. Thus instead of training the giant models from scratch we propose to bridge the existing strong models with a shared latent representation space. Specifically we propose a multimodality latent aligner with the pre-trained ImageBind model. Our latent aligner shares a similar core as the classifier guidance that guides the diffusion denoising process during inference time. Through carefully designed optimization strategy and loss functions we show the superior performance of our method on joint video-audio generation visual-steered audio generation and audio-steered visual generation tasks. The project website can be found at \href https://yzxing87.github.io/Seeing-and-Hearing/ https://yzxing87.github.io/Seeing-and-Hearing/ .
As an important pillar of underwater intelligence Marine Animal Segmentation (MAS) involves segmenting animals within marine environments. Previous methods don't excel in extracting long-range contextual features and overlook the connectivity between discrete pixels. Recently Segment Anything Model (SAM) offers a universal framework for general segmentation tasks. Unfortunately trained with natural images SAM does not obtain the prior knowledge from marine images. In addition the single-position prompt of SAM is very insufficient for prior guidance. To address these issues we propose a novel feature learning framework named Dual-SAM for high-performance MAS. To this end we first introduce a dual structure with SAM's paradigm to enhance feature learning of marine images. Then we propose a Multi-level Coupled Prompt (MCP) strategy to instruct comprehensive underwater prior information and enhance the multi-level features of SAM's encoder with adapters. Subsequently we design a Dilated Fusion Attention Module (DFAM) to progressively integrate multi-level features from SAM's encoder. Finally instead of directly predicting the masks of marine animals we propose a Criss-Cross Connectivity Prediction (C3P) paradigm to capture the inter-connectivity between discrete pixels. With dual decoders it generates pseudo-labels and achieves mutual supervision for complementary feature representations resulting in considerable improvements over previous techniques. Extensive experiments verify that our proposed method achieves state-of-the-art performances on five widely-used MAS datasets. The code is available at https://github.com/Drchip61/Dual SAM.
Camera-based person re-identification (ReID) systems have been widely applied in the field of public security. However cameras often lack the perception of 3D morphological information of human and are susceptible to various limitations such as inadequate illumination complex background and personal privacy. In this paper we propose a LiDAR-based ReID framework ReID3D that utilizes pre-training strategy to retrieve features of 3D body shape and introduces Graph-based Complementary Enhancement Encoder for extracting comprehensive features. Due to the lack of LiDAR datasets we build LReID the first LiDAR-based person ReID dataset which is collected in several outdoor scenes with variations in natural conditions. Additionally we introduce LReID-sync a simulated pedestrian dataset designed for pre-training encoders with tasks of point cloud completion and shape parameter learning. Extensive experiments on LReID show that ReID3D achieves exceptional performance with a rank-1 accuracy of 94.0 highlighting the significant potential of LiDAR in addressing person ReID tasks. To the best of our knowledge we are the first to propose a solution for LiDAR-based ReID. The code and dataset are available at https://github.com/GWxuan/ReID3D.
Implicit neural representations (INRs) were recently proposed as a new video compression paradigm with existing approaches performing on par with HEVC. However such methods only perform well in limited settings e.g. specific model sizes fixed aspect ratios and low-motion videos. We address this issue by proposing T-NeRV a hybrid video INR that combines frame-specific embeddings with GOP-specific features providing a lever for content-specific fine-tuning. We employ entropy-constrained training to jointly optimize our model for rate and distortion and demonstrate that T-NeRV can thereby automatically adjust this lever during training effectively fine-tuning itself to the target content. We evaluate T-NeRV on the UVG dataset where it achieves state-of-the-art results on the video representation task outperforming previous works by up to 3dB PSNR on challenging high-motion sequences. Further our method improves on the compression performance of previous methods and is the first video INR to outperform HEVC on all UVG sequences.
The pre-training architectures of large language models encompass various types including autoencoding models autoregressive models and encoder-decoder models. We posit that any modality can potentially benefit from a large language model as long as it undergoes vector quantization to become discrete tokens. Inspired by the General Language Model we propose a General Point Model (GPM) that seamlessly integrates autoencoding and autoregressive tasks in a point cloud transformer. This model is versatile allowing fine-tuning for downstream point cloud representation tasks as well as unconditional and conditional generation tasks. GPM enhances masked prediction in autoencoding through various forms of mask padding tasks leading to improved performance in point cloud understanding. Additionally GPM demonstrates highly competitive results in unconditional point cloud generation tasks even exhibiting the potential for conditional generation tasks by modifying the input's conditional information. Compared to models like Point-BERT MaskPoint and PointMAE our GPM achieves superior performance in point cloud understanding tasks. Furthermore the integration of autoregressive and autoencoding within the same transformer underscores its versatility across different downstream tasks.
L4D-Track: Language-to-4D Modeling Towards 6-DoF Tracking and Shape Reconstruction in 3D Point Cloud Stream
PDF ↗3D visual language multi-modal modeling plays an important role in actual human-computer interaction. However the inaccessibility of large-scale 3D-language pairs restricts their applicability in real-world scenarios. In this paper we aim to handle a real-time multi-task for 6-DoF pose tracking of unknown objects leveraging 3D-language pre-training scheme from a series of 3D point cloud video streams while simultaneously performing 3D shape reconstruction in current observation. To this end we present a generic Language-to-4D modeling paradigm termed L4D-Track that tackles zero-shot 6-DoF \underline Track ing and shape reconstruction by learning pairwise implicit 3D representation and multi-level multi-modal alignment. Our method constitutes two core parts. 1) Pairwise Implicit 3D Space Representation that establishes spatial-temporal to language coherence descriptions across continuous 3D point cloud video. 2) Language-to-4D Association and Contrastive Alignment enables multi-modality semantic connections between 3D point cloud video and language. Our method trained exclusively on public NOCS-REAL275 dataset achieves promising results on both two publicly benchmarks. This not only shows powerful generalization performance but also proves its remarkable capability in zero-shot inference.
Identifying the chemical structure from a graphical representation or image of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet existing methods for chemical structure recognition do not typically generalize well and show diminished effectiveness when confronted with domains where data is sparse or costly to generate such as hand-drawn molecule images. To address this limitation we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches our method provides atom-level localization and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency accuracy and atom-level entity prediction.
3D-aware GANs offer new capabilities for view synthesis while preserving the editing functionalities of their 2D counterparts. GAN inversion is a crucial step that seeks the latent code to reconstruct input images or videos subsequently enabling diverse editing tasks through manipulation of this latent code. However a model pre-trained on a particular dataset (e.g. FFHQ) often has difficulty reconstructing images with out-of-distribution (OOD) objects such as faces with heavy make-up or occluding objects. We address this issue by explicitly modeling OOD objects from the input in 3D-aware GANs. Our core idea is to represent the image using two individual neural radiance fields: one for the in-distribution content and the other for the out-of-distribution object. The final reconstruction is achieved by optimizing the composition of these two radiance fields with carefully designed regularization. We demonstrate that our explicit decomposition alleviates the inherent trade-off between reconstruction fidelity and editability. We evaluate reconstruction accuracy and editability of our method on challenging real face images and videos and showcase favorable results against other baselines.
We introduce MultiDiff a novel approach for consistent novel view synthesis of scenes from a single RGB image. The task of synthesizing novel views from a single reference image is highly ill-posed by nature as there exist multiple plausible explanations for unobserved areas. To address this issue we incorporate strong priors in form of monocular depth predictors and video-diffusion models. Monocular depth enables us to condition our model on warped reference images for the target views increasing geometric stability. The video-diffusion prior provides a strong proxy for 3D scenes allowing the model to learn continuous and pixel-accurate correspondences across generated images. In contrast to approaches relying on autoregressive image generation that are prone to drifts and error accumulation MultiDiff jointly synthesizes a sequence of frames yielding high-quality and multi-view consistent results -- even for long-term scene generation with large camera movements while reducing inference time by an order of magnitude. For additional consistency and image quality improvements we introduce a novel structured noise distribution. Our experimental results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging real-world datasets RealEstate10K and ScanNet. Finally our model naturally supports multi-view consistent editing without the need for further tuning.
Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system. Nonetheless its performance is challenged by images with degraded quality. Addressing this limitation we propose the Robust Segment Anything Model (RobustSAM) which enhances SAM's performance on low-quality images while preserving its promptability and zero-shot generalization. Our method leverages the pre-trained SAM model with only marginal parameter increments and computational requirements. The additional parameters of RobustSAM can be optimized within 30 hours on eight GPUs demonstrating its feasibility and practicality for typical research laboratories. We also introduce the Robust-Seg dataset a collection of 688K image-mask pairs with different degradations designed to train and evaluate our model optimally. Extensive experiments across various segmentation tasks and datasets confirm RobustSAM's superior performance especially under zero-shot conditions underscoring its potential for extensive real-world application. Additionally our method has been shown to effectively improve the performance of SAM-based downstream tasks such as single image dehazing and deblurring.
In this paper we focus on capturing closely interacted two-person motions from monocular videos an important yet understudied topic. Unlike less-interacted motions closely interacted motions contain frequently occurring inter-human occlusions which pose significant challenges to existing capturing algorithms. To address this problem our key observation is that close physical interactions between two subjects typically happen under very specific situations (e.g. handshake hug etc.) and such situational contexts contain strong prior semantics to help infer the poses of occluded joints. In this spirit we introduce reaction priors which are invertible neural networks that bi-directionally model the pose probability distributions of one person given the pose of the other. The learned reaction priors are then incorporated into a query-based pose estimator which is a decoder-only Transformer with self-attentions on both intra-joint and inter-joint relationships. We demonstrate that our design achieves considerably higher performance than previous methods on multiple benchmarks. What's more as existing datasets lack sufficient cases of close human-human interactions we also build a new dataset called Dual-Human to better evaluate different methods. Dual-Human contains around 2k sequences of closely interacted two-person motions each with synthetic multi-view renderings contact annotations and text descriptions. We believe that this new public dataset can significantly promote further research in this area.
Class-Incremental Learning (CIL) requires a learning system to continually learn new classes without forgetting. Despite the strong performance of Pre-Trained Models (PTMs) in CIL a critical issue persists: learning new classes often results in the overwriting of old ones. Excessive modification of the network causes forgetting while minimal adjustments lead to an inadequate fit for new classes. As a result it is desired to figure out a way of efficient model updating without harming former knowledge. In this paper we propose ExpAndable Subspace Ensemble (EASE) for PTM-based CIL. To enable model updating without conflict we train a distinct lightweight adapter module for each new task aiming to create task-specific subspaces. These adapters span a high-dimensional feature space enabling joint decision-making across multiple subspaces. As data evolves the expanding subspaces render the old class classifiers incompatible with new-stage spaces. Correspondingly we design a semantic-guided prototype complement strategy that synthesizes old classes' new features without using any old class instance. Extensive experiments on seven benchmark datasets verify EASE's state-of-the-art performance. Code is available at: https://github.com/sun-hailong/CVPR24-Ease
Coordinate based implicit neural representations have gained rapid popularity in recent years as they have been successfully used in image geometry and scene modeling tasks. In this work we present a novel use case for such implicit representations in the context of learning anatomically constrained face models. Actor specific anatomically constrained face models are the state of the art in both facial performance capture and performance retargeting. Despite their practical success these anatomical models are slow to evaluate and often require extensive data capture to be built. We propose the anatomical implicit face model; an ensemble of implicit neural networks that jointly learn to model the facial anatomy and the skin surface with high-fidelity and can readily be used as a drop in replacement to conventional blendshape models. Given an arbitrary set of skin surface meshes of an actor and only a neutral shape with estimated skull and jaw bones our method can recover a dense anatomical substructure which constrains every point on the facial surface. We demonstrate the usefulness of our approach in several tasks ranging from shape fitting shape editing and performance retargeting.
This paper focuses on synthesizing high-quality and complete textures directly on the surface of 3D models within 3D space. 2D diffusion-based methods face challenges in generating 2D texture maps due to the infinite possibilities of UV mapping for a given 3D mesh. Utilizing point clouds helps circumvent variations arising from diverse mesh topologies and UV mappings. Nevertheless achieving dense point clouds to accurately represent texture details poses a challenge due to limited computational resources. To address these challenges we propose an efficient octree-based diffusion pipeline called TexOct. Our method starts by sampling a point cloud from the surface of a given 3D model with each point containing texture noise values. We utilize an octree structure to efficiently represent this point cloud. Additionally we introduce an innovative octree-based diffusion model that leverages the denoising capabilities of the Denoising Diffusion Probabilistic Model (DDPM). This model gradually reduces the texture noise on the octree nodes resulting in the restoration of fine texture. Experimental results on ShapeNet demonstrate that TexOct effectively generates high-quality 3D textures in both unconditional and text / image-conditional scenarios.
Test-time adaptation (TTA) aims at adapting a model pre-trained on the labeled source domain to the unlabeled target domain. Existing methods usually focus on improving TTA performance under covariate shifts while neglecting semantic shifts. In this paper we delve into a realistic open-set TTA setting where the target domain may contain samples from unknown classes. Many state-of-the-art closed-set TTA methods perform poorly when applied to open-set scenarios which can be attributed to the inaccurate estimation of data distribution and model confidence. To address these issues we propose a simple but effective framework called unified entropy optimization (UniEnt) which is capable of simultaneously adapting to covariate-shifted in-distribution (csID) data and detecting covariate-shifted out-of-distribution (csOOD) data. Specifically UniEnt first mines pseudo-csID and pseudo-csOOD samples from test data followed by entropy minimization on the pseudo-csID data and entropy maximization on the pseudo-csOOD data. Furthermore we introduce UniEnt+ to alleviate the noise caused by hard data partition leveraging sample-level confidence. Extensive experiments on CIFAR benchmarks and Tiny-ImageNet-C show the superiority of our framework. The code is available at https://github.com/gaozhengqing/UniEnt.