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检索条件"主题词=3D Computer Vision"

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GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image

GeneAvatar: Generic Expression-Aware Volumetric Head Avatar ...

引用

IEEE/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Bao, Chong Zhang, Yinda Li, Yuan Zhang, Xiyu Yang, Bangbang Bao, Hujun Pollefeys, Marc Zhang, Guofeng Cui, Zhaopeng Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Google Mountain View CA 94043 USA Swiss Fed Inst Technol Zurich Switzerland ByteDance Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recently, we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars. However, due to the diversity of frameworks, there is no practical method to support high-level applications like 3d head avatar editing across different representations. In this paper, we propose a generic avatar editing approach that can be universally applied to various 3dMM-driving volumetric head avatars. To achieve this goal, we design a novel expression-aware modification generative model, which enables lift 2d editing from a single image to a consistent 3d modification field. To ensure the effectiveness of the generative modification process, we develop several techniques, including an expression-dependent modification distillation scheme to draw knowledge from the large-scale head avatar model and 2d facial texture editing tools, implicit latent space guidance to enhance model convergence, and a segmentation-based loss reweight strategy for fine-grained texture inversion. Extensive experiments demonstrate that our method delivers high-quality and consistent results across multiple expression and viewpoints. Project page: https://***/geneavatar/.

关键词： 3d computer vision avatar editing head avatar neural rendering

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ECSIC: Epipolar Cross Attention for Stereo Image Compression

ECSIC: Epipolar Cross Attention for Stereo Image Compression

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Woedlinger, Matthias Kotera, Jan Keglevic, Manuel Xu, Jan Sablatnig, Robert TU Wien Vienna Austria UTIA CAS Prague Czech Republic Deep Render London England

ISBN: (纸本)9798350318920;9798350318937

In this paper, we present ECSIC, a novel learned method for stereo image compression. Our proposed method compresses the left and right images in a joint manner by exploiting the mutual information between the images of the stereo image pair using a novel stereo cross attention (SCA) module and two stereo context modules. The SCA module performs cross-attention restricted to the corresponding epipolar lines of the two images and processes them in parallel. The stereo context modules improve the entropy estimation of the second encoded image by using the first image as a context. We conduct an extensive ablation study demonstrating the effectiveness of the proposed modules and a comprehensive quantitative and qualitative comparison with existing methods. ECSIC achieves state-of-the-art performance in stereo image compression on the two popular stereo image datasets Cityscapes and InStereo2k while allowing for fast encoding and decoding.

关键词： 3d computer vision Algorithms Algorithms and algorithms formulations Machine learning architectures

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Plane Segmentation in Outdoor Imagery: Unsupervised Training Using Synthetic data 6

Plane Segmentation in Outdoor Imagery: Unsupervised Training...

引用

6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge discovery (GeoAI)

作者： Liang, Haolin Newsam, Shawn Univ Calif Merced Comp Sci & Engn Merced CA 95343 USA Univ Calif Merced Merced CA USA

ISBN: (纸本)9798400703485

Plane segmentation in monocular images plays an important role in understanding the geometry of a 3d environment. However, most previous plane segmentation work focuses mostly on indoor environments since it is hard to create precise planar annotations of outdoor environments for training. In this work, we proposed a framework to automatically generate synthetic outdoor planar annotations data for any given 3d building model, that can be used for outdoor plane segmentation and geometry understanding tasks. We also conduct experiments to evaluate the applicability of the synthetic data to real-world scenarios. We show that plane segmentation models trained on synthetic outdoor data can also be used to understand real-world outdoor geometry.

关键词： synthetic data 3d computer vision plane segmentation

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SSP: Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds

SSP: Semi-signed prioritized neural fitting for surface reco...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Zhu, Rungsong Kang, di Hui, Ka-Hei Qian, Yue Qiu, Shi dong, Zhen Bao, Linchao Heng, Pheng-Ann Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Reconstructing 3d geometry from unoriented point clouds can benefit many downstream tasks. Recent shape modeling methods mostly adopt implicit neural representation to fit a signed distance field (SdF) and optimize the network by unsigned supervision. However, these methods occasionally have difficulty in finding the coarse shape for complicated objects, especially suffering from the "ghost" surfaces (i.e., fake surfaces that should not exist). To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision. To better recover high-fidelity details, a novel loss-based region sampling strategy and a progressive positional encoding (PE) method are applied to prioritize the optimization towards underfitting and complicated regions. Specifically, we voxelize and partition the object space into sign-known and sign-uncertain regions, in which different supervisions are applied. Besides, we adaptively adjust the sampling rate of each voxel according to the tracked reconstruction loss, so that the network can focus more on the complicated under-fitting regions. We conduct extensive experiments to demonstrate that our method achieves state-of-the-art performance compared to the existing fitting-based methods and comparable performance to learning-based methods on multiple datasets. The code is publicly available at https://***/Runsong123/SSP.

关键词： 3d computer vision Algorithms Applications Visualization

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Open-NeRF: Towards Open Vocabulary NeRF decomposition

Open-NeRF: Towards Open Vocabulary NeRF Decomposition

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Zhang, Hao Li, Fang Ahuja, Narendra Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350318920;9798350318937

In this paper, we address the challenge of decomposing Neural Radiance Fields (NeRF) into objects from an open vocabulary, a critical task for object manipulation in 3d reconstruction and view synthesis. Current techniques for NeRF decomposition involve a trade-off between the flexibility of processing open-vocabulary queries and the accuracy of 3d segmentation. We present, Open-vocabulary Embedded Neural Radiance Fields (Open-NeRF), that leverage large-scale, off-the-shelf, segmentation models like the Segment Anything Model (SAM) and introduce an integrate-and-distill paradigm with hierarchical embeddings to achieve both the flexibility of open-vocabulary querying and 3d segmentation accuracy. Open-NeRF first utilizes large-scale foundation models to generate hierarchical 2d mask proposals from varying viewpoints. These proposals are then aligned via tracking approaches and integrated within the 3d space and subsequently distilled into the 3d field. This process ensures consistent recognition and granularity of objects from different viewpoints, even in challenging scenarios involving occlusion and indistinct features. Our experimental results show that the proposed Open-NeRF1 outperforms state-of-the-art methods such as LERF [16] and FFd [18] in open-vocabulary scenarios. Open-NeRF offers a promising solution to NeRF decomposition, guided by open-vocabulary queries, enabling novel applications in robotics and vision-language interaction in open-world 3d scenes. Please find the code at https://***/haoz19/Open-NeRF.

关键词： 3d computer vision Algorithms

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LongFormer: Longitudinal Transformer for Alzheimer's disease Classification with Structural MRIs

LongFormer: Longitudinal Transformer for Alzheimer's Disease...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Chen, Qiuhui Fu, Qiang Bai, Hao Hong, Yi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Structural magnetic resonance imaging (sMRI), especially longitudinal sMRI, is often used to monitor and capture disease progression during the clinical diagnosis of Alzheimer's disease (Ad). However, current methods neglect Ad's progressive nature and have mostly relied on a single image for recognizing Ad. In this paper, we consider the problem of leveraging the longitudinal MRIs of a subject for Ad classification. To address the challenges of missing data, data demand, and subtle changes over time in learning longitudinal 3d MRIs, we propose a novel model LongFormer, which is a hybrid 3d CNN and transformer design to learn from image and longitudinal flow pairs. Our model can fully leverage all images in a dataset and effectively fuse spatiotemporal features for classification. We evaluate our model on three datasets, i.e., AdNI, OASIS, and AIBL, and compare it to eight baseline algorithms. Our proposed LongFormer achieves state-of-the-art performance in classifying Ad and NC subjects from all three public datasets. Our source code is available online at https://***/Qybc/LongFormer.

关键词： 3d computer vision Algorithms Algorithms Image recognition and understanding

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HMP: Hand Motion Priors for Pose and Shape Estimation from Video

HMP: Hand Motion Priors for Pose and Shape Estimation from V...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： duran, Enes Kocabas, Muhammed Choutas, Vasileios Fan, Zicong Black, Michael J. MPI Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350318920;9798350318937

Understanding how humans interact with the world necessitates accurate 3d hand pose estimation, a task complicated by the hand's high degree of articulation, frequent occlusions, self-occlusions, and rapid motions. While most existing methods rely on single-image inputs, videos have useful cues to address aforementioned issues. However, existing video-based 3d hand datasets are insufficient for training feedforward models to generalize to in-the-wild scenarios. On the other hand, we have access to large human motion capture datasets which also include hand motions, e.g. AMASS. Therefore, we develop a generative motion prior specific for hands, trained on the AMASS dataset which features diverse and high-quality hand motions. This motion prior is then employed for video-based 3d hand motion estimation following a latent optimization approach. Our integration of a robust motion prior significantly enhances performance, especially in occluded scenarios. It produces stable, temporally consistent results that surpass conventional single-frame methods. We demonstrate our method's efficacy via qualitative and quantitative evaluations on the HO3d and dexYCB datasets, with special emphasis on an occlusion-focused subset of HO3d. Code is available at https://***

关键词： 3d computer vision Algorithms Algorithms Biometrics body pose face gesture

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Letting 3d Guide the Way: 3d Guided 2d Few-Shot Image Classification

Letting 3D Guide the Way: 3D Guided 2D Few-Shot Image Classi...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Chen, Jiajing Yang, Minmin Velipasalar, Senem Syracuse Univ Elect Engn & Comp Sci Dept Syracuse NY 13210 USA

ISBN: (纸本)9798350318920;9798350318937

Existing few-shot image classification networks aim to perform prediction on images belonging to classes that were not seen during training, with only a few labeled images, which are randomly picked from the same image pool as the support set. However, this traditional approach has two main issues: (i) in real-world applications, since support images are randomly picked, the angle they were captured from can be very different from that of the query image, causing the images to look very different and making it hard to match them;(ii) since support and query images, for both training and testing, are sampled from the same image pool, models can overfit the dataset, especially if the image pool contains images with similar color, texture or view angle. Thus, good performance on a dataset does not reflect a model's real ability. To address these issues, we propose a novel few-shot learning approach referred to as the 3d guided 2d (3dG2d) few-shot image classification. In our proposed approach, the queries are 2d images, and the support set is composed of 3d mesh data, providing different views of an object, in contrast to randomly picked images providing a single view. From each 3d mesh, 14 projection images are generated from different angles. Thus, these projections have significant variance among themselves. To address this challenge, we also propose the Angle Inference Module (AIM), which is used to infer the view angle of a query image so that more attention is given to projection images corresponding to the same view angle as the query image to achieve better prediction performance. We perform experiments on ModelNet40, Toys4K and ShapeNet datasets with 4-fold cross validation, and show that our 3dG2d few-shot classification approach consistently outperforms the state-of-the-art baselines.

关键词： 3d computer vision Algorithms Algorithms and algorithms formulations Machine learning architectures

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S4C: Self-Supervised Semantic Scene Completion With Neural Fields 11

S4C: Self-Supervised Semantic Scene Completion With Neural F...

引用

International Conference in 3d vision (3dV)

作者： Hayler, Adrian Wimbauer, Felix Muhle, dominik Rupprecht, Christian Cremers, daniel Tech Univ Munich Munich Germany MCML Munich Germany Univ Oxford Oxford England

ISBN: (纸本)9798350362466;9798350362459

3d semantic scene understanding is a fundamental challenge in computer vision. It enables mobile agents to autonomously plan and navigate arbitrary environments. SSC formalizes this challenge as jointly estimating dense geometry and semantic information from sparse observations of a scene. Current methods for SSC are generally trained on 3d ground truth based on aggregated LidAR scans. This process relies on special sensors and annotation by hand which are costly and do not scale well. To overcome this issue, our work presents the first self-supervised approach to SSC called S4C that does not rely on 3d ground truth data. Our proposed method can reconstruct a scene from a single image and only relies on videos and pseudo segmentation ground truth generated from off-the-shelf image segmentation network during training. Unlike existing methods, which use discrete voxel grids, we represent scenes as implicit semantic fields. This formulation allows querying any point within the camera frustum for occupancy and semantic class. Our architecture is trained through rendering-based self-supervised losses. Nonetheless, our method achieves performance close to fully supervised state-of-the-art methods. Additionally, our method demonstrates strong generalization capabilities and can synthesize accurate segmentation maps for far away viewpoints.

关键词： 3d computer vision computer vision kitti kitti-360 machine learning scene completion scene reconstruction semantic scene completion sscbench

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ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields

ParticleNeRF: A Particle-Based Encoding for Online Neural Ra...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Abou-Chakra, Jad dayoub, Feras Sunderhauf, Niko Queensland Univ Technol Brisbane Australia Univ Adelaide Adelaide Australia

ISBN: (纸本)9798350318920;9798350318937

While existing Neural Radiance Fields (NeRFs) for dynamic scenes are offline methods with an emphasis on visual fidelity, our paper addresses the online use case that prioritises real-time adaptability. We present ParticleNeRF, a new approach that dynamically adapts to changes in the scene geometry by learning an up-to-date representation online, every 200 ms. ParticleNeRF achieves this using a novel particle-based parametric encoding. We couple features to particles in space and backpropagate the photometric reconstruction loss into the particles' position gradients, which are then interpreted as velocity vectors. Governed by a lightweight physics system to handle collisions, this lets the features move freely with the changing scene geometry. We demonstrate ParticleNeRF on various dynamic scenes containing translating, rotating, articulated, and deformable objects. ParticleNeRF is the first online dynamic NeRF and achieves fast adaptability with better visual fidelity than brute-force online InstantNGP and other baseline approaches on dynamic scenes with online constraints. Videos of our system can be found at the anonymous project website https://***/view/particlenerf.

关键词： 3d computer vision Algorithms Applications Robotics

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