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φ-SfT: Shape-from-Template with a Physics-Based Deformation Model

φ-SfT: Shape-from-Template with a Physics-Based Deformation...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kairanda, Navami Tretschk, Edith Elgharib, Mohamed Theobalt, Christian Golyanik, Vladislav Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ SIC Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Shape-from-Template (SIT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods.

关键词： Deformable models Surface reconstruction Three-dimensional displays Cameras Rendering (computer graphics) Data models Surface texture

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Geometric Anchor Correspondence Mining with Uncertainty Modeling for Universal Domain Adaptation

Geometric Anchor Correspondence Mining with Uncertainty Mode...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Liang Lou, Yihang He, Jianzhong Bai, Tao Deng, Minghua Peking Univ Sch Math Sci Beijing Peoples R China Huawei Technol Intelligent Vis Dept Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both shared "known" samples and private "unknown" samples. Previous works rarely explore the intrinsic geometrical relationship between the two domains, and they manually set a threshold for the overconfident closed-world classifier to reject "unknown" samples. Therefore, in this paper, we propose a Geometric anchorguided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues. Specifically, we first develop a random walk-based anchor mining strategy together with a high-order attention mechanism to build correspondence across domains. Then a global joint local domain alignment paradigm is designed, i.e., geometric adversarial learning for global distribution calibration and subgraph-level contrastive learning for local region aggregation. Toward accurate target private samples detection, GATE introduces a universal incremental classifier by modeling the energy uncertainty. We further efficiently generate novel categories by manifold mixup, and minimize the open-set entropy to learn the "unknown" threshold adaptively. Extensive experiments on three benchmarks demonstrate that GATE significantly outperforms previous state-of-the-art UniDA methods.

关键词： Representation learning Manifolds Adaptation models computer vision Uncertainty Computational modeling Logic gates

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Keep it Accurate and Diverse: Enhancing Action recognition Performance by Ensemble Learning

Keep it Accurate and Diverse: Enhancing Action Recognition P...

引用

ieee conference on computer vision and pattern recognition (CVPR)

作者： Bagheri, Mohammad Ali Gao, Qigang Escalera, Sergio Clapes, Albert Nasrollahi, Kamal Holte, Michael B. Moeslund, Thomas B. Dalhousie Univ Fac Comp Sci Halifax NS Canada UAB Comp Vis Ctr Barcelona 08193 Spain Univ Barcelona Dept Appl Methemat E-08007 Barcelona Spain Visual Anal People VAP Lab DK-9000 Aalborg Denmark

ISBN: (纸本)9781467367592

The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective. The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing enhanced performance of the proposed methodology.

关键词： Accuracy computer vision Feature extraction Histograms Trajectory Videos Visualization

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StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions

StyleMesh: Style Transfer for Indoor 3D Scene Reconstruction...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hoellein, Lukas Johnson, Justin Niessner, Matthias Tech Univ Munich Munich Germany Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We apply style transfer on mesh reconstructions of indoor scenes. This enables VR applications like experiencing 3D environments painted in the style of a favorite artist. Style transfer typically operates on 2D images, making stylization of a mesh challenging. When optimized over a variety of poses, stylization patterns become stretched out and inconsistent in size. On the other hand, model-based 3D style transfer methods exist that allow stylization from a sparse set of images, but they require a network at inference time. To this end, we optimize an explicit texture for the reconstructed mesh of a scene and stylize it jointly from all available input images. Our depth- and angle-aware optimization leverages surface normal and depth data of the underlying mesh to create a uniform and consistent stylization for the whole scene. Our experiments show that our method creates sharp and detailed results for the complete scene without view-dependent artifacts. Through extensive ablation studies, we show that the proposed 3D awareness enables style transfer to be applied to the 3D domain of a mesh. Our method I can be used to render a stylized mesh in real-time with traditional rendering pipelines.

关键词： Surface reconstruction Solid modeling computer vision Three-dimensional displays Pipelines Rendering (computer graphics) Real-time systems

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ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

ViP-LLaVA: Making Large Multimodal Models Understand Arbitra...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cai, Mu Liu, Haotian Mustikovela, Siva Karthik Meyer, Gregory P. Chai, Yuning Park, Dennis Lee, Yong Jae Univ Wisconsin Madison WI 53706 USA Cruise LLC San Francisco CA USA

ISBN: (纸本)9798350353006

While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge, we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow". Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings, yet achieves state-of-the-art performance on region-understanding tasks like Visual7W, PointQA, and Visual Commonsense Reasoning benchmark. Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain. Code, data, and model are publicly available.

关键词： Large Language Models Large Multimodal Models Multimodal Benchmark Region-level Understanding vision-language models Visual Commonsense Reasoning Visual Prompts

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Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Dave Zhenyu Gholami, Ali Niesner, Matthias Chang, Angel X. Tech Univ Munich Munich Germany Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene;the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detection and description problems, we propose Scan2Cap, an end-to-end trained method, to detect objects in the input scene and describe them in natural language. We use an attention mechanism that generates descriptive tokens while referring to the related components in the local context. To reflect object relations (i.e. relative spatial relations) in the generated captions, we use a message passing graph module to facilitate learning object relation features. Our method can effectively localize and describe 3D objects in scenes from the ScanRefer dataset, outperforming 2D baseline methods by a significant margin (27.61% CiDEr@0.5IoU improvement).

关键词： Location awareness Three-dimensional displays Message passing Natural languages Pipelines computer architecture Object detection

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DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

DivCo: Diverse Conditional Image Synthesis via Contrastive G...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Rui Ge, Yixiao Choi, Ching Lam Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China NVIDIA NVIDIA AI Technol Ctr Hong Kong Peoples R China Xidian Univ Sch CST Xian Peoples R China

ISBN: (纸本)9781665445092

Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. To solve this issue, previous works [47, 22] mainly focused on encouraging the correlation between the latent codes and their generated images, while ignoring the relations between images generated from various latent codes. The recent MSGAN [27] tried to encourage the diversity of the generated image but only considers "negative" relations between the image pairs. In this paper, we propose a novel DivCo framework to properly constrain both "positive" and "negative" relations between the generated images specified in the latent space. To the best of our knowledge, this is the first attempt to use contrastive learning for diverse conditional image synthesis. A novel latent-augmented contrastive loss is introduced, which encourages images generated from adjacent latent codes to be similar and those generated from distinct latent codes to be dissimilar. The proposed latent-augmented contrastive loss is well compatible with various cGAN architectures. Extensive experiments demonstrate that the proposed DivCo can produce more diverse images than state-of-the-art methods without sacrificing visual quality in multiple unpaired and paired image generation tasks. Training code and pretrained models are available at https://***/ruiliu-ai/DivCo.

关键词： Training Visualization computer vision Codes Correlation Image synthesis Generative adversarial networks

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

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

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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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SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

SelfSAGCN: Self-Supervised Semantic Alignment for Graph Conv...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Xu Deng, Cheng Dang, Zhiyuan Wei, Kun Yan, Junchi Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks.

关键词： Deep learning computer vision Convolution Computational modeling Semantics Benchmark testing Feature extraction

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Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis 32

Improving Few-Shot User-Specific Gaze Adaptation via Gaze Re...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yu, Yu Liu, Gang Odobez, Jean-Marc Idiap Res Inst CH-1920 Martigny Switzerland Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

ISBN: (纸本)9781728132938

As an indicator of human attention gaze is a subtle behavioral cue which can be exploited in many applications. However, inferring 3D gaze direction is challenging even for deep neural networks given the lack of large amount of data (groundtruthing gaze is expensive and existing datasets use different setups) and the inherent presence of gaze biases due to person-specific difference. In this work, we address the problem ofperson-specific gaze model adaptation from only a few reference training samples. The main and novel idea is to improve gaze adaptation by generating additional training samples through the synthesis of gaze-redirected eye images from existing reference samples. In doing so, our contributions are threefold: (i) we design our gaze redirection framework from synthetic data, allowing us to benefit from aligned training sample pairs to predict accurate inverse mapping fields;(ii) we proposed a self-supervised approach for domain adaptation;(iii) we exploit the gaze redirection to improve the performance of person-specific gaze estimation. Extensive experiments on two public datasets demonstrate the validity of our gaze re-targeting and gaze estimation framework.

关键词： And Body Pose Face Gesture Image and Video Synthesis vision Applications and Systems

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