pwc-archive/papers-with-abstracts huggingface dataset:
Polarized Color Screen Matting
Authors: Kenji Enomoto, Scott Cohen, Brian Price, TJ Rhodes
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Matting

    This paper considers the long-standing problem of extracting alpha mattes from video using a known background. While various color-based or polarization-based approaches have been studied in past decades, the problem remains ill-posed because the solutions solely rely on either color or polarization. We introduce Polarized Color Screen Matting, a single-shot, per-pixel matting theory for alpha matte and foreground color recovery using both color and polarization cues. Through a theoretical analysis of our diffuse-specular polarimetric compositing equation, we derive practical closed-form matting methods with their solvability conditions. Our theory concludes that an alpha matte can be extracted without manual corrections using off-the-shelf equipment such as an LCD monitor, polarization camera, and unpolarized lights with calibrated color. Experiments on synthetic and real-world datasets verify the validity of our theory and show the capability of our matting methods on real videos with quantitative and qualitative comparisons to color-based and polarization-based matting methods.
Splatter-360: Generalizable 360 Gaussian Splatting for Wide-baseline Panoramic Images
Authors: Zheng Chen, Chenming Wu, Zhelun Shen, Chen Zhao, Weicai Ye, Haocheng Feng, Errui Ding, Song-Hai Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, NeRF

    Wide-baseline panoramic images are frequently used in applications like VR and simulations to minimize capturing labor costs and storage needs. However, synthesizing novel views from these panoramic images in real time remains a significant challenge, especially due to panoramic imagery's high resolution and inherent distortions. Although existing 3D Gaussian splatting (3DGS) methods can produce photo-realistic views under narrow baselines, they often overfit the training views when dealing with wide-baseline panoramic images due to the difficulty in learning precise geometry from sparse 360^ \circ  views. This paper presents Splatter-360, a novel end-to-end generalizable 3DGS framework designed to handle wide-baseline panoramic images. Unlike previous approaches, Splatter-360 performs multi-view matching directly in the spherical domain by constructing a spherical cost volume through a spherical sweep algorithm, enhancing the network's depth perception and geometry estimation. Additionally, we introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images and integrate cross-view attention to improve feature interactions across multiple viewpoints. This enables robust 3D-aware feature representations and real-time rendering capabilities. Experimental results on the HM3D and Replica demonstrate that Splatter-360 significantly outperforms state-of-the-art NeRF and 3DGS methods (e.g., PanoGRF, MVSplat, DepthSplat, and HiSplat) in both synthesis quality and generalization performance for wide-baseline panoramic images. Code and trained models are available at https://3d-aigc.github.io/Splatter-360/.
PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter
Authors: Yaohua Zha, Yanzi Wang, Hang Guo, Jinpeng Wang, Tao Dai, Bin Chen, Zhihao Ouyang, Xue Yuerong, Ke Chen, Shu-Tao Xia
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Mamba

    Applying pre-trained models to assist point cloud understanding has recently become a mainstream paradigm in 3D perception. However, existing application strategies are straightforward, utilizing only the final output of the pre-trained model for various task heads. It neglects the rich complementary information in the intermediate layer, thereby failing to fully unlock the potential of pre-trained models. To overcome this limitation, we propose an orthogonal solution: Point Mamba Adapter (PMA), which constructs an ordered feature sequence from all layers of the pre-trained model and leverages Mamba to fuse all complementary semantics, thereby promoting comprehensive point cloud understanding. Constructing this ordered sequence is non-trivial due to the inherent isotropy of 3D space. Therefore, we further propose a geometry-constrained gate prompt generator (G2PG) shared across different layers, which applies shared geometric constraints to the output gates of the Mamba and dynamically optimizes the spatial order, thus enabling more effective integration of multi-layer information. Extensive experiments conducted on challenging point cloud datasets across various tasks demonstrate that our PMA elevates the capability for point cloud understanding to a new level by fusing diverse complementary intermediate features. Code is available at https://github.com/zyh16143998882/PMA.
DKC: Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identification
Authors: Zhenyu Cui, Jiahuan Zhou, Yuxin Peng
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Lifelong learning, Person Re-Identification, Transfer Learning

    Lifelong person re-identification (LReID) aims to match the same person using sequentially collected data. However, due to the long-term nature of lifelong learning, the inevitable changes in human clothes prevent the model from relying on unified discriminative information (e.g., clothing style) to match the same person in the streaming data, demanding differentiated cloth-irrelevant information. Unfortunately, existing LReID methods typically fail to leverage such knowledge resulting in the exacerbation of catastrophic forgetting issues. Therefore, in this paper, we focus on a challenging practical task called Cloth-Hybrid Lifelong Person Re-identification (CH-LReID), which requires matching the same person wearing different clothes using sequentially collected data. A Differentiated Knowledge Consolidation (DKC) framework is designed to unify and balance distinct knowledge across streaming data. The core idea is to adaptively balance differentiated knowledge and compatibly consolidate cloth-relevant and cloth-irrelevant information. To this end, a Differentiated Knowledge Transfer (DKT) module and a Latent Knowledge Consolidation (LKC) module are designed to adaptively discover differentiated new knowledge, while eliminating the derived domain shift of old knowledge via reconstructing the old latent feature space, respectively. Then, to further alleviate the catastrophic conflict between differentiated new and old knowledge, we further propose a Dual-level Distribution Alignment (DDA) module to align the distribution of discriminative knowledge at both the instance level and the fine-grained level. Extensive experiments on multiple benchmarks demonstrate the superiority of our method against existing methods in both CH-LReID and traditional LReID tasks. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/DKC-CVPR2025.
FrugalNeRF: Fast Convergence for Extreme Few-shot Novel View Synthesis without Learned Priors
Authors: Chin-Yang Lin, Chung-Ho Wu, Chang-Han Yeh, Shih-Han Yen, Cheng Sun, Yu-Lun Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Scene Reconstruction, NeRF, Novel View Synthesis, Scheduling

    Neural Radiance Fields (NeRF) face significant challenges in extreme few-shot scenarios, primarily due to overfitting and long training times. Existing methods, such as FreeNeRF and SparseNeRF, use frequency regularization or pre-trained priors but struggle with complex scheduling and bias. We introduce FrugalNeRF, a novel few-shot NeRF framework that leverages weight-sharing voxels across multiple scales to efficiently represent scene details. Our key contribution is a cross-scale geometric adaptation scheme that selects pseudo ground truth depth based on reprojection errors across scales. This guides training without relying on externally learned priors, enabling full utilization of the training data. It can also integrate pre-trained priors, enhancing quality without slowing convergence.  Experiments on LLFF, DTU, and RealEstate-10K show that FrugalNeRF outperforms other few-shot NeRF methods while significantly reducing training time, making it a practical solution for efficient and accurate 3D scene reconstruction.
Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention
Authors: Kyungmin Jo, Jooyeol Yun, Jaegul Choo
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation

    While large-scale text-to-image diffusion models enable the generation of high-quality, diverse images from text prompts, these prompts struggle to capture intricate details, such as textures, preventing the user intent from being reflected. This limitation has led to efforts to generate images conditioned on user-provided images, referred to as image prompts. Recent work modifies the self-attention mechanism to impose image conditions in generated images by replacing or concatenating the keys and values from the image prompt. This enables the self-attention layer to work like a cross-attention layer, generally used to incorporate text prompts.In this paper, we identify two common issues in existing methods of modifying self-attention that hinder diffusion models from reflecting the image prompt. By addressing these issues, we propose a novel method that generates images that properly reflect the details of image prompts. First, existing approaches often neglect the importance of image prompts in classifier-free guidance, which directs the model towards the intended conditions and away from those undesirable. Specifically, current methods use image prompts as both desired and undesired conditions, causing conflicting signals. To resolve this, we propose conflict-free guidance by using image prompts only as desired conditions, ensuring that the generated image faithfully reflects the image prompt.In addition, we observe that the two most common self-attention modifications involve a trade-off between the realism of the generated image and alignment with the image prompt, achieved by selectively using keys and values from both images. Specifically, selecting more keys and values from the image prompt improves alignment, while selecting more from the generated image enhances realism. To balance both, we propose an alternative self-attention modification method, Stratified Attention, which jointly uses keys and values from both images rather than selecting between them.Through extensive experiments across three distinct image generation tasks, we demonstrate that the proposed method outperforms existing image-prompting models in faithfully reflecting the image prompt.
Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion
Authors: Haoyu Wang, Le Wang, Sanping Zhou, Jingyi Tian, Zheng Qin, Yabing Wang, Gang Hua, Wei Tang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Decoder, Denoising

    Embodied localization based on vision and natural language dialogues presents a persistent challenge in embodied intelligence. Existing methods often approach this task as an image translation problem, leveraging encoder-decoder architectures to predict heatmaps. However, these methods frequently experience a deficiency in accuracy, largely due to their heavy reliance on resolution. To address this issue, we introduce CGD, a novel framework that utilizes causality guided diffusion model to directly model coordinate distributions. Specifically, CGD employs a denoising network to regress coordinates, while integrating causal learning modules, namely back-door adjustment (BDA) and front-door adjustment (FDA) to mitigate confounders during the diffusion process. This approach reduces the dependency on high resolution for improving accuracy, while effectively minimizing spurious correlations, thereby promoting unbiased learning. By guiding the denoising process with causal adjustments, CGD offers flexible control over intensity, ensuring seamless integration with diffusion models. Experimental results demonstrate that CGD outperforms state-of-the-art methods across all metrics. Additionally, we also evaluate CGD in a multi-shot setting, achieving consistently high accuracy.
SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling
Authors: Qi Zhu, Jiangwei Lao, Deyi Ji, Junwei Luo, Kang Wu, Yingying Zhang, Lixiang Ru, Jian Wang, Jingdong Chen, Ming Yang, Dong Liu, Feng Zhao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Language Modeling, Language Modelling

    Open-world interpretation aims to accurately localize and recognize all objects within images by vision-language models (VLMs). While substantial progress has been made in this task for natural images, the advancements for remote sensing (RS) images still remain limited, primarily due to these two challenges. 1) Existing RS semantic categories are limited, particularly for pixel-level interpretation datasets. 2) Distinguishing among diverse RS spatial regions solely by language space is challenging due to the dense and intricate spatial distribution in open-world RS imagery. To address the first issue, we develop a fine-grained RS interpretation dataset, Sky-SA, which contains 183,375 high-quality local image-text pairs with full-pixel manual annotations, covering 1,763 category labels, exhibiting richer semantics and higher density than previous datasets. Afterwards, to solve the second issue, we introduce the vision-centric principle for vision-language modeling. Specifically, in the pre-training stage, the visual self-supervised paradigm is incorporated into image-text alignment, reducing the degradation of general visual representation capabilities of existing paradigms. Then, we construct a visual-relevance knowledge graph across open-category texts and further develop a novel vision-centric image-text contrastive loss for fine-tuning with text prompts. This new model, denoted as SkySense-O, demonstrates impressive zero-shot capabilities on a thorough evaluation encompassing 14 datasets over 4 tasks, from recognizing to reasoning and classification to localization. Specifically, it outperforms the latest models such as SegEarth-OV, GeoRSCLIP, and VHM by a large margin, i.e., 11.95%, 8.04% and 3.55% on average respectively.
Understanding Fine-tuning CLIP for Open-vocabulary Semantic Segmentation in Hyperbolic Space
Authors: Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, Wei Shen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Open Vocabulary Semantic Segmentation, Open-Vocabulary Semantic Segmentation, Segmentation, Semantic Segmentation

    CLIP, a foundational vision-language model, has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing the text encoder preserves its powerful embeddings, recent studies show that fine-tuning both the text and image encoders jointly significantly enhances segmentation performance, especially for classes from open sets. In this work, we explain this phenomenon from the perspective of hierarchical alignment, since during fine-tuning, the hierarchy level of image embeddings shifts from image-level to pixel-level. We achieve this by leveraging hyperbolic space, which naturally encoders hierarchical structures. Our key observation is that, during fine-tuning, the hyperbolic radius of CLIP's text embeddings decreases, facilitating better alignment with the pixel-level hierarchical structure of visual data. Building on this insight, we propose HyperCLIP, a novel fine-tuning strategy that adjusts the hyperbolic radius of the text embeddings through scaling transformations. By doing so, HyperCLIP equips CLIP with segmentation capability while introducing only a small number of learnable parameters. Our experiments demonstrate that HyperCLIP achieves state-of-the-art performance on open-vocabulary semantic segmentation tasks across three benchmarks, while fine-tuning only approximately 4% of the total parameters of CLIP. More importantly, we observe that after adjustment, CLIP's text embeddings exhibit a relatively fixed hyperbolic radius across datasets, suggesting that the segmentation task has a characteristic level in hyperbolic space.
Compositional Targeted Multi-Label Universal Perturbations
Authors: Hassan Mahmood, Ehsan Elhamifar
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Generating targeted universal perturbations for multi-label recognition is a combinatorially hard problem that requires exponential time and space complexity. To address the problem, we propose a compositional framework. We show that a simple independence assumption on label-wise universal perturbations naturally leads to an efficient optimization that requires learning affine convex cones spanned by label-wise universal perturbations, significantly reducing the problem complexity to linear time and space. During inference, the framework allows generating universal perturbations for novel combinations of classes in constant time. We demonstrate the scalability of our method on large datasets and target sizes, evaluating its performance on NUS-WIDE, MS-COCO, and OpenImages using state-of-the-art multi-label recognition models. Our results show that our approach outperforms baselines and achieves results comparable to methods with exponential complexity.
RAEncoder: A Label-Free Reversible Adversarial Examples Encoder for Dataset Intellectual Property Protection
Authors: Fan Xing, Zhuo Tian, Xuefeng Fan, Xiaoyi Zhou
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Self-Supervised Learning

    Reversible Adversarial Examples (RAE) are designed to protect the intellectual property of datasets. Such examples can function as imperceptible adversarial examples to erode the model performance of unauthorized users while allowing authorized users to remove the adversarial perturbations and recover the original samples for normal model training. With the rise of Self-Supervised Learning (SSL), an increasing number of unlabeled datasets and pre-trained encoders are available in the community. However, existing RAE methods not only rely on well-labeled datasets for training Supervised Learning (SL) models but also exhibit poor adversarial transferability when attacking SSL pre-trained encoders. To address these challenges, we propose RAEncoder, the first framework for RAEs without the need for labeled samples. RAEncoder aims to generate universal adversarial perturbations by targeting SSL pre-trained encoders. Unlike traditional RAE approaches, the pre-trained encoder outputs the feature distribution of the protected dataset rather than classification labels, enhancing both the attack success rate and transferability of RAEs. Extensive experiments are conducted on six pre-trained encoders and four SL models, covering aspects such as imperceptibility and transferability. Our results demonstrate that RAEncoder effectively protects unlabeled datasets from malicious infringements. Additional robustness experiments further confirm the security of RAEncoder in practical application scenarios.
TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance
Authors: Mushui Liu, Dong She, Jingxuan Pang, Qihan Huang, Jiacheng Ying, Wanggui He, Yuanlei Hou, Siming Fu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Image Generation

    Subject-driven image personalization has seen notable advancements, especially with the advent of the ReferenceNet paradigm. ReferenceNet excels in integrating image reference features, making it highly applicable in creative and commercial settings. However, current implementations of ReferenceNet primarily operate as latent-level feature extractors, which limit their potential. This constraint hinders the provision of appropriate features to the denoising backbone across different timesteps, leading to suboptimal image consistency. In this paper, we revisit the extraction of reference features and propose TFCustom, a model framework designed to focus on reference image features at different temporal steps and frequency levels. Specifically, we firstly propose synchronized ReferenceNet to extract reference image features while simultaneously optimizing noise injection and denoising for the reference image. We also propose a time-aware frequency feature refinement module that leverages high- and low-frequency filters, combined with time embeddings, to adaptively select the degree of reference feature injection. Additionally, to enhance the similarity between reference objects and the generated image, we introduce a novel reward-based loss that encourages greater alignment between the reference and generated images. Experimental results demonstrate state-of-the-art performance in both multi-object and single-object reference generation, with significant improvements in texture and textual detail generation over existing methods.
Let's Chorus: Partner-aware Hybrid Song-Driven 3D Head Animation
Authors: Xiumei Xie, Zikai Huang, Wenhao Xu, Peng Xiao, Xuemiao Xu, Huaidong Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Singing is a vital form of human emotional expression and social interaction, distinguished from speech by its richer emotional nuances and freer expressive style. Thus, investigating 3D facial animation driven by singing holds significant research value. Our work focuses on 3D singing facial animation driven by mixed singing audio, and to the best of our knowledge, no prior studies have explored this area. Additionally, the absence of existing 3D singing datasets poses a considerable challenge. To address this, we collect a novel audiovisual dataset, ChorusHead which features synchronized mixed vocal audio and pseudo-3D flame motions for chorus singing. In addition, We propose a partner-aware 3D chorus head generation framework driven by mixed audio inputs. The proposed framework extracts emotional features from the background music and dependence between singers and models the head movement in a latent space from the Variational Autoencoder (VAE), enabling diverse interactive head animation generation. Extensive experimental results demonstrate that our approach effectively generates 3D facial animations of interacting singers, achieving notable improvements in realism and handling background music interference with strong robustness.
NTClick: Achieving Precise Interactive Segmentation With Noise-tolerant Clicks
Authors: Chenyi Zhang, Ting Liu, Xiaochao Qu, Luoqi Liu, Yao Zhao, Yunchao Wei
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Interactive Segmentation, Segmentation

    Interactive segmentation is a pivotal task in computer vision, focused on predicting precise masks with minimal user input. Although the click has recently become the most prevalent form of interaction due to its flexibility and efficiency, its advantages diminish as the complexity and details of target objects increase because it's time-consuming and user-unfriendly to precisely locate and click on narrow, fine regions. To tackle this problem, we propose NTClick, a powerful click-based interactive segmentation method capable of predicting accurate masks even with imprecise user clicks when dealing with intricate targets. We first introduce a novel interaction form called Noist-tolerant Click, a type of click that does not require user's precise localization when selecting fine regions. Then, we design a two-stage workflow, consisting of an Explicit Coarse Perception network for initial estimation and a High Resolution Refinement network for final classification. Quantitative results across extensive datasets demonstrate that NTClick not only maintains an efficient and flexible interaction mode but also significantly outperforms existing methods in segmentation accuracy.