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共 21179 条记录，以下是311-320 订阅

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GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zheng, Shunyuan Zhou, Boyao Shao, Ruizhi Liu, Boning Zhang, Shengping Nie, Liqiang Liu, Yebin Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353006

We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed. The code is available at https://***/aipixel/GPS-Gaussian.

关键词： Rendering (computer graphics)

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StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

StableVITON: Learning Semantic Correspondence with Latent Di...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Jeongho Gu, Gyojung Park, Minho Park, Sunghyun Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON out-performs the baselines in qualitative and quantitative eval-uation, showing promising quality in arbitrary person images. Our code is available at https://***/ rlawjdghek/StableVITON.

关键词： computer vision Diffusion Model Virtual Try-On

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SaCo Loss: Sample-wise Affinity Consistency for vision-Language Pre-training

SaCo Loss: Sample-wise Affinity Consistency for Vision-Langu...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Sitong Tan, Haoru Tian, Zhuotao Chen, Yukang Qi, Xiaojuan Jia, Jiaya CUHK Hong Kong Peoples R China HKU Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350353006

vision-language pre-training (VLP) aims to learn joint representations of vision and language modalities. The contrastive paradigm is currently dominant in this field. However, we observe a notable misalignment phenomenon, that is, the affinity between samples has an obvious disparity across different modalities, namely "Affinity Inconsistency Problem". Our intuition is that, for a well-aligned model, two images that look similar to each other should have the same level of similarity as their corresponding texts that describe them. In this paper, we first investigate the reason of this inconsistency problem. We discover that the lack of consideration for sample-wise affinity consistency across modalities in existing training objectives is the central cause. To address this problem, we propose a novel loss function, named Sample-wise affinity Consistency (SaCo) loss, which is designed to enhance such consistency by minimizing the distance between image embedding similarity and text embedding similarity for any two samples. Our SaCo loss can be easily incorporated into existing vision-language models as an additional loss due to its complementarity for most training objectives. In addition, considering that pre-training from scratch is computationally expensive, we also provide a more efficient way to continuously pre-train on a converged model by integrating our loss. Experimentally, the model trained with our SaCo loss significantly outperforms the baseline on a variety of vision and language tasks.

关键词： Affinity Consistency vision-Language Pre-training

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PIGEON: Predicting Image Geolocations

PIGEON: Predicting Image Geolocations

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Haas, Lukas Skreta, Michal Alberti, Silas Finn, Chelsea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350353006

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. Additionally, our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization;the first model, PIGEON, is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans, ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers, winning all six games. Our second model, PIGEOTTO, differs in that it is trained on a dataset of images from Flickr and Wikipedia, achieving state-of-the-art results on a wide range of image geolocalization benchmarks, outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate, planet-scale image geolocalization systems. Our code is available on GitHub.(1)

关键词： Blind Experiment CLIP Clustering computer vision GeoGuessr Haversine Image Geolocalization Location Refinement Multi-Modal Multi-Task Multi-Task Pretraining Photo Geolocalization Pretraining Retrieval Semantic Geocells Visual Place recognition Voronoi

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DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses

DVMNet: Computing Relative Pose for Unseen Objects Beyond Hy...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Chen Zhang, Tong Dang, Zheng Salzmann, Mathieu Ecole Polytech Fed Lausanne Lausanne Switzerland ClearSpace SA Renens Switzerland

ISBN: (纸本)9798350353006

Determining the relative pose of an object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically approximate the continuous pose representation with a large number of discrete pose hypotheses, which incurs a computationally expensive process of scoring each hypothesis at test time. By contrast, we present a Deep Voxel Matching Network (DVMNet) that eliminates the need for pose hypotheses and computes the relative object pose in a single pass. To this end, we map the two input RGB images, reference and query, to their respective voxelized 3D representations. We then pass the resulting voxels through a pose estimation module, where the voxels are aligned and the pose is computed in an end-to-end fashion by solving a least-squares problem. To enhance robustness, we introduce a weighted closest voxel algorithm capable of mitigating the impact of noisy voxels. We conduct extensive experiments on the CO3D, LINEMOD, and Objaverse datasets, demonstrating that our method delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods. Our code is released at: https://***/sailor-z/DVMNet/.

关键词： 3D computer vision Multi-View Geometry Object Pose Estimation

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Progressive Semantic-Guided vision Transformer for Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Shiming Hou, Wenjin Khan, Salman Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Huazhong Univ Sci & Technol Wuhan Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2.

关键词： zero-shot learning

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Visual Program Distillation: Distilling Tools and Programmatic Reasoning into vision-Language Models

Visual Program Distillation: Distilling Tools and Programmat...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hu, Yushi Stretcu, Otilia Lu, Chun-Ta Viswanathan, Krishnamurthy Hata, Kenji Luo, Enming Krishna, Ranjay Fuxman, Ariel Google Res Mountain View CA 94043 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350353006

Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However, generated programs are error-prone: they omit necessary steps, include spurious ones, and are unable to recover when the specialized models give incorrect outputs. Moreover, they require loading multiple models, incurring high latency and computation costs. We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs, which are then executed and verified to identify a correct one. It translates each correct program into a language description of the reasoning steps, which are then distilled into a VLM. Extensive experiments show that VPD improves the VLM's ability to count, understand spatial relations, and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs, achieving state-of-the-art performance across complex vision tasks, including MMBench, OK-VQA, A-OKVQA, TallyQA, POPE, and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally, experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data.

关键词： cross-modal reasoning distillation instruction tuning large language model multimodal synthetic data tools vision-language model visual programming

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SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large vision Language Models

SC-Tune: Unleashing Self-Consistent Referential Comprehensio...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yue, Tongtian Cheng, Jie Guo, Longteng Dai, Xingyuan Zhao, Zijia He, Xingjian Xiong, Gang Lv, Yisheng Liu, Jing CASIA Lab Cognit & Decis Intelligence Complex Syst Beijing Peoples R China CASIA State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Recent trends in Large vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level referential comprehension. In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process. This capability significantly mirrors the precision and reliability of fine-grained visual-language understanding. Our findings reveal that the self-consistency level of existing LVLMs falls short of expectations, posing limitations on their practical applicability and potential. To address this gap, we introduce a novel fine-tuning paradigm named Self-Consistency Tuning (SC-Tune). It features the synergistic learning of a cyclic describer-locator system. This paradigm is not only data-efficient but also exhibits generalizability across multiple LVLMs. Through extensive experiments, we demonstrate that SC-Tune significantly elevates performance across a spectrum of object-level vision-language benchmarks and maintains competitive or improved performance on image-level vision-language benchmarks. Both our model and code will be publicly available at https://***/ivattyue/SC-Tune.

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GLID: Pre-training a Generalist Encoder-Decoder vision Model

GLID: Pre-training a Generalist Encoder-Decoder Vision Model

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Jihao Zheng, Jinliang Liu, Yu Li, Hongsheng CUHK MMLab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China CPII InnoHK Hong Kong Peoples R China Tsinghua Univ Inst AI Ind Res AIR Shanghai Peoples R China

ISBN: (纸本)9798350353006

This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approaches, e.g., Masked Autoencoder, have shown success in transfer learning, task-specific sub-architectures are still required to be appended for different downstream tasks, which cannot enjoy the benefits of large-scale pre-training. GLID overcomes this challenge by allowing the pre-trained generalist encoder-decoder to be fine-tuned on various vision tasks with minimal task-specific architecture modifications. In the GLID training scheme, pre-training pretext task and other downstream tasks are modeled as "query-to-answer" problems, including the pre-training pretext task and other downstream tasks. We pre-train a task-agnostic encoder-decoder with query-mask pairs. During fine-tuning, GLID maintains the pre-trained encoder-decoder and queries, only replacing the topmost linear transformation layer with task-specific linear heads. This minimizes the pretrain-finetune architecture inconsistency and enables the pre-trained model to better adapt to downstream tasks. GLID achieves competitive performance on various vision tasks, including object detection, image segmentation, pose estimation, and depth estimation, outper-forming or matching specialist models such as Mask2Former, DETR, ViTPose, and BinsFormer.

关键词： Image segmentation

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EgoGen: An Egocentric Synthetic Data Generator

EgoGen: An Egocentric Synthetic Data Generator

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Gen Zhao, Kaifeng Zhang, Siwei Lyu, Xiaozhong Dusmanu, Mihai Zhang, Yan Pollefeys, Marc Tang, Siyu Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA

ISBN: (纸本)9798350353006

Understanding the world in first-person view is fundamental in Augmented Reality (AR). This immersive perspective brings dramatic visual changes and unique challenges compared to third-person views. Synthetic data has empowered third-person-view vision models, but its application to embodied egocentric perception tasks remains largely unexplored. A critical challenge lies in simulating natural human movements and behaviors that effectively steer the embodied cameras to capture a faithful egocentric representation of the 3D world. To address this challenge, we introduce EgoGen, a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. Combined with collision-avoiding motion primitives and a two-stage reinforcement learning approach, our motion synthesis model offers a closed-loop solution where the embodied perception and movement of the virtual human are seamlessly coupled. Compared to previous works, our model eliminates the need for a predefined global path, and is directly applicable to dynamic environments. Combined with our easy-to-use and scalable data generation pipeline, we demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views. EgoGen will be fully open-sourced, offering a practical solution for creating realistic egocentric training data and aiming to serve as a useful tool for egocentric computer vision research.

关键词： autonomous virtual humans egocentric vision reinforcement learning synthetic data

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