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Noisy-Correspondence Learning for Text-to-Image Person Re-Identification

Noisy-Correspondence Learning for Text-to-Image Person Re-Id...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yang Qin Yingke Chen Dezhong Peng Xi Peng Joey Tianyi Zhou Peng Hu College of Computer Science Sichuan University Chengdu China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne UK Sichuan Newstrong UHD Video Technology Co. Ltd. Chengdu China Chengdu Ruibei Yingte Information Technology Company Ltd. Chengdu China Centre for Frontier AI Research (CFAR) and Institute of High Performance Computing (IHPC) A *STAR Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query. Although numerous TIReID methods have been proposed and achieved promising performance, they implicitly assume the training image-text pairs are correctly aligned, which is not always the case in real-world scenarios. In practice, the image-text pairs inevitably exist under-correlated or even false-correlated, a.k.a noisy correspondence (NC), due to the low quality of the images and annotation errors. To address this problem, we propose a novel Robust Dual Embedding method (RDE) that can learn robust visual-semantic associations even with NC. Specifically, RDE consists of two main components: 1) A Confident Consensus Division (CCD) module that leverages the dual-grained decisions of dual embedding modules to obtain a consensus set of clean training data, which enables the model to learn correct and reliable visual-semantic associations. 2) A Triplet Alignment Loss (TAL) relaxes the conventional Triplet Ranking loss with the hardest negative samples to a log-exponential upper bound over all negative ones, thus preventing the model collapse under NC and can also focus on hard-negative samples for promising performance. We conduct extensive experiments on three public benchmarks, namely CUHK-PEDES, ICFG-PEDES, and RSTPReID, to evaluate the performance and robustness of our RDE. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on all three datasets. Code is available at https://***/QinYang79/RDE.

关键词： Training computer vision Upper bound Text to image Training data Robustness Data models

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Gaussian Shell Maps for Efficient 3D Human Generation

Gaussian Shell Maps for Efficient 3D Human Generation

引用

conference on computer vision and pattern recognition (CVPR)

作者： Rameen Abdal Wang Yifan Zifan Shi Yinghao Xu Ryan Po Zhengfei Kuang Qifeng Chen Dit-Yan Yeung Gordon Wetzstein Stanford University USA HKUST

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Efficient generation of 3D digital humans is important in several industries, including virtual reality, social media, and cinematic production. 3D generative adversarial net-works (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for generated assets. Current 3D GAN architectures, however, typically rely on volume representations, which are slow to render, thereby hampering the GAN training and requiring multi- view-inconsistent 2D upsam-plers. Here, we introduce Gaussian Shell Maps (GSMs) as a framework that connects SOTA generator network archi-tectures with emerging 3D Gaussian rendering primitives using an articulable multi shell-based scaffold. In this set-ting, a CNN generates a 3D texture stack with features that are mapped to the shells. The latter represent inflated and deflated versions of a template surface of a digital human in a canonical body pose. Instead of rasterizing the shells directly, we sample 3D Gaussians on the shells whose at-tributes are encoded in the texture features. These Gaus-sians are efficiently and differentiably rendered. The ability to articulate the shells is important during GAN training and, at inference time, to deform a body into arbitrary user-defined poses. Our efficient rendering scheme bypasses the need for view-inconsistent upsamplers and achieves high-quality multi-view consistent renderings at a native resolution of 512 ×512 pixels. We demonstrate that GSMs suc-cessfully generate 3D humans when trained on single-view datasets, including SHHQ and DeepFashion. Project Page: ***/GaussianShellMaps

关键词： Training Industries Three-dimensional displays Social networking (online) Virtual reality Production Rendering (computer graphics)

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Compact 3D Gaussian Representation for Radiance Field

Compact 3D Gaussian Representation for Radiance Field

引用

conference on computer vision and pattern recognition (CVPR)

作者： Joo Chan Lee Daniel Rho Xiangyu Sun Jong Hwan Ko Eunbyung Park Sungkyunkwan University KT

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Neural Radiance Fields (NeRFs) have demonstrated re-markable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hin-ders the widespread adoption of NeRFs is the computational bottleneck due to the volumetric rendering. On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the ras-terization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality. However, a significant draw-back arises as 3DGS entails a substantial number of 3D Gaussians to maintain the high fidelity of the rendered images, which requires a large amount of memory and stor-age. To address this critical issue, we place a specific emphasis on two key objectives: reducing the number of Gaussian points without sacrificing performance and compressing the Gaussian attributes, such as view-dependent color and covariance. To this end, we propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field rather than relying on spherical harmonics. Finally, we learn codebooks to compactly represent the geometric attributes of Gaussian by vector quantization. With model compression techniques such as quantization and entropy coding, we consistently show over 25× reduced storage and enhanced rendering speed, while maintaining the quality of the scene representation, compared to 3DGS. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compact-ness, and real-time rendering. Our project page is available at https://mainco/***/c3dgs/.

关键词： Training Visualization Three-dimensional displays Image color analysis Vector quantization Pipelines Rendering (computer graphics)

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3DToonify: Creating Your High-Fidelity 3D Stylized Avatar Easily from 2D Portrait Images

3DToonify: Creating Your High-Fidelity 3D Stylized Avatar Ea...

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conference on computer vision and pattern recognition (CVPR)

作者： Yifang Men Hanxi Liu Yuan Yao Miaomiao Cui Xuansong Xie Zhouhui Lian Institute for Intelligent Computing Alibaba Group Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Visual content creation has aroused a surge of interest given its applications in mobile photography and AR/VR. Portrait style transfer and 3D recovery from monocular images as two representative tasks have so far evolved independently. In this paper, we make a connection between the two, and tackle the challenging task of 3D portrait styl-ization - modeling high-fidelity 3D stylized avatars from captured 2D portrait images. However, naively combining the techniques from the two isolated areas may suf-fer from either inadequate stylization or absence of 3D as-sets. To this end, we propose 3DToonify, a new framework that introduces a progressive training scheme to achieve 3D style adaption on spatial neural representation (SNR). SNR is constructed with implicit fields and they are dynamically optimized by the progressive training scheme, which consists of three stages: guided prior learning, deformable geometry adaption and explicit texture adaption. In this way, stylized geometry and texture are learned in SNR in an explicit and structured way with only a single stylized exemplar needed. Moreover, our method obtains style-adaptive underlying structures (i.e., deformable geometry and exaggerated texture) and view-consistent styl-ized avatar rendering from arbitrary novel viewpoints. Both qualitative and quantitative experiments have been conducted to demonstrate the effectiveness and superiority of our method for automatically generating exemplar-guided 3D stylized avatars.

关键词： Geometry Training Photography Visualization Solid modeling Three-dimensional displays Avatars

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En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data

En3D: An Enhanced Generative Model for Sculpting 3D Humans f...

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conference on computer vision and pattern recognition (CVPR)

作者： Yifang Men Biwen Lei Yuan Yao Miaomiao Cui Zhouhui Lian Xuansong Xie Institute for Intelligent Computing Alibaba Group Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present En3D, an enhanced generative scheme for sculpting high-quality 3D human avatars. Unlike previous works that rely on scarce 3D datasets or limited 2D collections with imbalanced viewing angles and imprecise pose priors, our approach aims to develop a zero-shot 3D generative scheme capable of producing visually realistic, ge-ometrically accurate and content-wise diverse 3D humans without directly relying on pre-existing 3D or 2D assets. To address this challenge, we introduce a meticulously crafted workflow that implements accurate physical modeling to learn the enhanced 3D generative model from synthetic 2D data. During inference, we integrate optimization modules to bridge the gap between realistic appearances and coarse 3D shapes. Specifically, En3D comprises three modules: a 3D generator that accurately models generalizable 3D humans with realistic appearance from synthesized balanced, diverse, and structured human images; a geometry sculptor that enhances shape quality using multi-view normal constraints for intricate human structure; and a texturing module that disentangles explicit texture maps with fidelity and editability, leveraging semantical UV partitioning and a differentiable rasterizer: Experimental results show that our approach significantly outperforms prior works in terms of image quality, geometry accuracy and content diversity. We also showcase the applicability of our generated avatars for animation and editing, as well as the scalability of our approach for content-style free adaptation.

关键词： Geometry Solid modeling Three-dimensional displays Accuracy Shape Avatars Scalability

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What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs

What You See is What You GAN: Rendering Every Pixel for High...

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conference on computer vision and pattern recognition (CVPR)

作者： Alex Trevithick Matthew Chan Towaki Takikawa Umar Iqbal Shalini De Mello Manmohan Chandraker Ravi Ramamoorthi Koki Nagano University of California San Diego NVIDIA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet, the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution, which sacrifices multiview consistency and the quality of resolved geometry. Consequently, 3D GANs have not yet been able to fully resolve the rich 3D geometry present in 2D images. In this work, we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly “render every pixel” of the full-resolution image during training and inference without post-processing superresolution in 2D. Together with our strategy to learn high-quality surface geometry, our method synthesizes high-resolution 3D geometry and strictly view-consistent images while maintaining image quality on par with baselines relying on post-processing super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ and AFHQ, setting a new standard for unsupervised learning of 3D shapes in 3D GANs.

关键词： Geometry Training Three-dimensional displays Shape Superresolution Rendering (computer graphics) Generative adversarial networks

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GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models

GaussianDreamer: Fast Generation from Text to 3D Gaussians b...

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conference on computer vision and pattern recognition (CVPR)

作者： Taoran Yi Jiemin Fang Junjie Wang Guanjun Wu Lingxi Xie Xiaopeng Zhang Wenyu Liu Qi Tian Xinggang Wang School of EIC Huazhong University of Science and Technology Huawei Inc. School of CS Huazhong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In recent times, the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency, but their quality and generalization are limited as trainable 3D data is expensive and hard to obtain. 2D diffusion models enjoy strong abilities of generalization and fine generation, but 3D consistency is hard to guarantee. This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation. A fast 3D object gener-ation framework, named as GaussianDreamer, is proposed, where the 3D diffusion model provides priors for initial-ization and the 2D diffusion model enriches the geometry and appearance. Operations of noisy point growing and color perturbation are introduced to enhance the initialized Gaussians. Our GaussianDreamer can generate a high-quality 3D instance or 3D avatar within 15 minutes on one GPU, much faster than previous methods, while the generated instances can be directly rendered in real time. Demos and code are available at https://***/gaussiandreamer/.

关键词： Geometry Solid modeling Three-dimensional displays Perturbation methods Graphics processing units Diffusion models Rendering (computer graphics)

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GenN2N: Generative NeRF2NeRF Translation

GenN2N: Generative NeRF2NeRF Translation

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conference on computer vision and pattern recognition (CVPR)

作者： Xiangyue Liu Han Xue Kunming Luo Ping Tan Li Yi Hong Kong University of Science and Technology Tsinghua University Shanghai Artificial Intelligence Laboratory Shanghai Qi Zhi Institute

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present GenN2N, a unified NeRF-to-NeRF translation framework for various NeRF translation tasks such as text-driven NeRF editing, colorization, super-resolution, in-painting, etc. Unlike previous methods designed for individual translation tasks with task-specific schemes, GenN2N achieves all these NeRF editing tasks by employing a plug-and-play image-to-image translator to perform editing in the 2D domain and lifting 2D edits into the 3D NeRF space. Since the 3D consistency of 2D edits may not be assured, we propose to model the distribution of the underlying 3D edits through a generative model that can cover all possible edited NeRFs. To model the distribution of 3D edited NeRFs from 2D edited images, we carefully design a VAE-GAN that encodes images while decoding NeRFs. The latent space is trained to align with a Gaussian distribution and the NeRFs are supervised through an adversarial loss on its renderings. To ensure the latent code does not depend on 2D viewpoints but truly reflects the 3D edits, we also regularize the latent code through a contrastive learning scheme. Extensive experiments on various editing tasks show GenN2N, as a universal framework, performs as well or better than task-specific specialists while possessing flexible generative power. More results on our project page: https://***/GenN2N/.

关键词： Solid modeling Three-dimensional displays Codes Superresolution Gaussian distribution Neural radiance field Rendering (computer graphics)

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Auto- Train-Once: Controller Network Guided Automatic Network Pruning from Scratch

Auto- Train-Once: Controller Network Guided Automatic Networ...

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conference on computer vision and pattern recognition (CVPR)

作者： Xidong Wu Shangqian Gao Zeyu Zhang Zhenzhen Li Runxue Bao Yanfu Zhang Xiaoqian Wang Heng Huang University of Pittsburgh University of Arizona Bosch Center for AI GE HealthCare College of William and Mary Purdue University University of Maryland College Park

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that re-quire domain-specific expertise, making their widespread adoption challenging. To address the limitation, the Only-Train-Once (OTO) and OTOv2 are proposed to eliminate the need for additional fine-tuning steps by directly training and compressing a general DNN from scratch. Never-theless, the static design of optimizers (in OTO) can lead to convergence issues of local optima. In this paper, we proposed the Auto-Train-Once (A TO), an innovative net-work pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. During the model training phase, our approach not only trains the tar-get model but also leverages a controller network as an ar-chitecture generator to guide the learning of target model weights. Furthermore, we developed a novel stochastic gradient algorithm that enhances the coordination between model training and controller network training, thereby im-proving pruning performance. We provide a comprehen-sive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures (including ResNet18, ResNet34, ResNet50, ResNet56, and MobileNetv2) on standard benchmark datasets (CIFAR-10, CIFAR-100, and ImageNet). The code is available at https: 11 g i thub. comlxidon gwul Auto Train Once.

关键词： Training Costs Computational modeling Heuristic algorithms Stochastic processes computer architecture Benchmark testing

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Versatile Navigation Under Partial Observability via Value-Guided Diffusion Policy

Versatile Navigation Under Partial Observability via Value-G...

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conference on computer vision and pattern recognition (CVPR)

作者： Gengyu Zhang Hao Tang Yan Yan Department of Computer Science Illinois Institute of Technology USA Robotics Institute Carnegie Mellon University USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Route planning for navigation under partial observability plays a crucial role in modern robotics and autonomous driving. Existing route planning approaches can be categorized into two main classes: traditional autoregressive and diffusion-based methods. The former often fails due to its myopic nature, while the latter either assumes full observability or struggles to adapt to unfamiliar scenarios, due to strong couplings with behavior cloning from experts. To address these deficiencies, we propose a versatile diffusion-based approach for both 2D and 3D route planning under partial observability. Specifically, our value-guided diffusion policy first generates plans to predict actions across various timesteps, providing ample foresight to the planning. It then employs a differentiable planner with state estimations to derive a value function, directing the agent's exploration and goal-seeking behaviors without seeking experts while explicitly addressing partial observability. During inference, our policy is further enhanced by a best-plan-selection strategy, substantially boosting the planning success rate. Moreover, we propose projecting point clouds, derived from RGB-D inputs, onto 2D grid-based bird-eye- view maps via semantic segmentation, generalizing to 3D environments. This simple yet effective adaption enables zero-shot transfer from 2D-trained policy to 3D, cutting across the laborious training for 3D policy, and thus certifying our versatility. Experimental results demonstrate our superior performance, particularly in navigating situations beyond expert demonstrations, surpassing state-of-the-art autoregressive and diffusion-based baselines for both 2D and 3D scenarios.

关键词： Training Point cloud compression Three-dimensional displays Uncertainty Navigation Semantic segmentation Planning

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