pwc-archive/papers-with-abstracts huggingface dataset:
AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video
Authors: Noah Stier, Alex Rich, Pradeep Sen, Tobias Höllerer
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction

    Recent image-based 3D reconstruction methods have achieved excellent quality for indoor scenes using 3D convolutional neural networks. However, they rely on a high-resolution grid in order to achieve detailed output surfaces, which is quite costly in terms of compute time, and it results in large mesh sizes that are more expensive to store, transmit, and render. In this paper we propose a new solution to this problem, using adaptive sampling. By re-formulating the final layers of the network, we are able to analytically bound the local surface complexity, and set the local sample rate accordingly. Our method, AniGrad, achieves an order of magnitude reduction in both surface extraction latency and mesh size, while preserving mesh accuracy and detail.
Action Detail Matters: Refining Video Recognition with Local Action Queries
Authors: Mengmeng Wang, Zeyi Huang, Xiangjie Kong, Guojiang Shen, Guang Dai, Jingdong Wang, Yong liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Recognition, Temporal Action Localization, Video Recognition

    Video action recognition involves interpreting both global context and specific details to accurately identify actions. While previous models are effective at capturing spatiotemporal features, they often lack a focused representation of key action details. To address this, we introduce \nameo, a framework designed for refining video action recognition through integrated global and local feature learning. Inspired by human visual cognition theory, our approach balances the focus on both broad contextual changes and action-specific details, minimizing the influence of irrelevant background noise. We first employ learnable action queries to selectively emphasize action-relevant regions without requiring region-specific labels. Next, these queries are learned by a local action streaming branch that enables progressive query propagation. Moreover, we introduce a parameter-free feature interaction mechanism for effective multi-scale interaction between global and local features with minimal additional overhead. Extensive experiments demonstrate that \name achieves state-of-the-art performance across multiple action recognition datasets, validating its effectiveness and robustness in handling action-relevant details.
Active Hyperspectral Imaging Using an Event Camera
Authors: Bohan Yu, Jinxiu Liang, Zhuofeng Wang, Bin Fan, Art Subpa-Asa, Boxin Shi, Imari Sato
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Spectral Reconstruction

    Hyperspectral imaging plays a critical role in numerous scientific and industrial fields. Conventional hyperspectral imaging systems often struggle with the trade-off between capture speed, spectral resolution, and bandwidth, particularly in dynamic environments. In this work, we present a novel event-based active hyperspectral imaging system designed for real-time capture with low bandwidth in dynamic scenes. By combining an event camera with a dynamic illumination strategy, our system achieves unprecedented temporal resolution while maintaining high spectral fidelity, all at a fraction of the bandwidth requirements of traditional systems. Unlike basis-based methods that sacrifice spectral resolution for efficiency, our approach enables continuous spectral sampling through an innovative "sweeping rainbow" illumination pattern synchronized with a rotating mirror array. The key insight is leveraging the sparse, asynchronous nature of event cameras to encode spectral variations as temporal contrasts, effectively transforming the spectral reconstruction problem into a series of geometric constraints. Extensive evaluations of both synthetic and real data demonstrate that our system outperforms state-of-the-art methods in temporal resolution while maintaining competitive spectral reconstruction quality.
Robust-MVTON: Learning Cross-Pose Feature Alignment and Fusion for Robust Multi-View Virtual Try-On
Authors: Nannan Zhang, Yijiang Li, Dong Du, Zheng Chong, Zhengwentai Sun, Jianhao Zeng, Yusheng Dai, Zhengyu Xie, Hairui Zhu, Xiaoguang Han
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Virtual Try-on

    This paper tackles the emerging challenge of multi-view virtual try-on, utilizing both front- and back-view clothing images as inputs. Extending frontal try-on methods to a multi-view context is not straightforward. Simply concatenating the two input views or encoding their features for a generative model, such as a diffusion model, often fails to produce satisfactory results. The main challenge lies in effectively extracting and fusing meaningful clothing features from these input views. Existing explicit warping based methods, which establish direct correspondence between input and target views, tend to introduce artifacts, particularly when there is a significant disparity between the input and target views. Conversely, implicit encoding based methods often lose spatial information about clothing, resulting in outputs that lack detail. To overcome these challenges, we propose Robust-MVTON, an end-to end method for robust and high-quality multi-view try-ons. Our approach introduces a novel cross-pose feature alignment technique to guide the fusion of clothing features and incorporates a newly designed loss function for training. With the fused multi-scale clothing features, we employ a coarse-to-fine diffusion model to generate realistic and detailed results. Extensive experiments conducted on the Deepfashion and MPV datasets affirm the superiority of our method, achieving state-of-the-art performance.
BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation
Authors: Shengze Wang, Jiefeng Li, Tianye Li, Ye Yuan, Henry Fuchs, Koki Nagano, Shalini De Mello, Michael Stengel
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Pose Estimation, Depth Estimation, Human Mesh Recovery, Pose Estimation, Translation

    Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images taken from afar, but they break down as the person moves close to the camera. Moreover, current methods fail to achieve both accurate 3D pose and 2D alignment at the same time. Error is mainly introduced by inaccurate perspective projection heuristically derived from orthographic parameters. To resolve this long-standing challenge, we present our method BLADE which accurately recovers perspective parameters from a single image without heuristic assumptions. We start from the inverse relationship between perspective distortion and the person's Z-translation T_z, and we show that T_z can be reliably estimated from the image. We then discuss the important role of T_z for accurate human mesh recovery estimated from close-range images. Finally, we show that, once T_z and the 3D human mesh are estimated, one can accurately recover the focal length and full 3D translation. Extensive experiments on standard benchmarks and real-world close-range images show that our method accurately recovers projection parameters from a single image, and consequently attains state-of-the-art accuracy on both 3D pose estimation and 2D alignment for a wide range of images.
FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Authors: Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: DeepFake Detection, Domain Generalization, Face Swapping, Representation Learning

    Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data. In this paper, we identify a new type of model bias in the frequency domain, termed spectral bias, where detectors overly rely on specific frequency bands, restricting their ability to generalize across unseen forgeries. To address this, we propose FreqDebias, a frequency debiasing framework that mitigates spectral bias through two complementary strategies. First, we introduce a novel Forgery Mixup (Fo-Mixup) augmentation, which dynamically diversifies frequency characteristics of training samples. Second, we incorporate a dual consistency regularization (CR), which enforces both local consistency using class activation maps (CAMs) and global consistency through a von Mises-Fisher (vMF) distribution on a hyperspherical embedding space. This dual CR mitigates over-reliance on certain frequency components by promoting consistent representation learning under both local and global supervision. Extensive experiments show that FreqDebias significantly enhances cross-domain generalization and outperforms state-of-the-art methods in both cross-domain and in-domain settings.
Enhancing Diversity for Data-free Quantization
Authors: Kai Zhao, Zhihao Zhuang, Miao Zhang, Chenjuan Guo, Yang Shu, Bin Yang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Data Free Quantization, Diversity, Quantization

    Model quantization is an effective way to compress deep neural networks and accelerate the inference time on edge devices. Existing quantization methods usually require original data for calibration during the compressing process, which may be inaccessible due to privacy issues. A common way is to generate calibration data to mimic the origin data. However, the generators in these methods have the mode collapse problem, making them unable to synthesize diverse data. To solve this problem, we leverage the information from the full-precision model and enhance both inter-class and intra-class diversity for generating better calibration data, by devising a multi-layer features mixer and normalization flow based attention. Besides, novel regulation losses are proposed to make the generator produce diverse data with more patterns from the perspective of activated feature values and for the quantized model to learn better clip ranges adaptive to our diverse calibration data. Extensive experiments show that our method achieves state-of-the-art quantization results for both Transformer and CNN architectures. In addition, we visualize the generated data to verify that our strategies can effectively handle the mode collapse issue. Our codes are available at https://anonymous.4open.science/r/DFQ-84E6 and will be publicly available.
Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment
Authors: Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Sanath Narayan, Ankit Singh, Noel E. O'Connor
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Semantic Similarity, Semantic Textual Similarity

    Recent contrastive multimodal vision-language models like CLIP have demonstrated robust open-world semantic understanding, becoming the standard image backbones for vision-language applications. However, recent findings suggest high semantic similarity between well-trained unimodal encoders, which raises a key question: Are semantically similar embedding spaces separated only by simple projection transformations? To validate this, we propose a novel framework that aligns vision and language using frozen unimodal encoders. It involves selecting semantically similar encoders in the latent space, curating a concept-rich dataset of image-caption pairs, and training simple MLP projectors. We evaluated our approach on various tasks involving both strong unimodal vision (0-shot localization) and language encoders (multi-lingual, long context) and show that simple Projectors retain unimodal capabilities in joint embedding space. Furthermore, our best model, utilizing DINOv2 and All-Roberta-Large text encoder, achieves 76(%) accuracy on ImageNet with a 20-fold reduction in data and 65-fold reduction in compute requirements compared to multimodal alignment where models are trained from scratch. The proposed framework enhances the accessibility of multimodal model development while enabling flexible adaptation across diverse scenarios. Code and curated datasets are available at github.com/mayug/freeze-align.
Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering
Authors: Biplab Das, Viswanath Gopalakrishnan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Camouflaged Object Segmentation, Object, Semantic Segmentation

    In this work, we introduce Camouflage Anything, a novel and robust approach to generate camouflaged datasets. To the best of our knowledge, we are the first to apply Controlled Out-painting and Representation Engineering for generating realistic camouflaged images with an objective to hide any segmented object coming from a generic or salient database. Our proposed method uses a novel control design to out-paint a given segmented object, with a camouflaged background. We also uncover the role of representation engineering in enhancing the quality of generated camouflage datasets. We address the limitations of existing metrics FID and KID in capturing the 'camouflage quality' ,by proposing a novel metric namely, CamOT. CamOT uses Optimal Transport between foreground & background Gaussian Mixture Models (GMM) of concerned camouflaged object to assign an image quality score. Furthermore, we conduct LoRA-based fine-tuning of the robust BiRefNet baseline with our generated camouflaged datasets, leading to notable improvements in camouflaged object segmentation accuracy. The experimental results showcase the efficacy and potential of Camouflage Anything, outperforming existing methods in camouflaged generation tasks.
Towards Explainable and Unprecedented Accuracy in Matching Challenging Finger Crease Patterns
Authors: Zhenyu Zhou, Chengdong Dong, Ajay Kumar
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Mobile Security

    The primary obstacle in realizing the full potential of finger crease biometrics is the accurate identification of deformed knuckle patterns, often resulting from completely contactless imaging. Current methods struggle significantly with this task, yet accurate matching is crucial for applications ranging from forensic investigations, such as child abuse cases, to surveillance and mobile security. To address this challenge, our study introduces the largest publicly available dataset of deformed knuckle patterns, comprising 805,768 images from 351 subjects. We also propose a novel framework to accurately match knuckle patterns, even under severe pose deformations, by recovering interpretable knuckle crease keypoint feature templates. These templates can dynamically uncover graph structure and feature similarity among the matched correspondences. Our experiments, using the most challenging protocols, illustrate significantly outperforming results for matching such knuckle images. For the first time, we present and evaluate a theoretical model to estimate the uniqueness of 2D finger knuckle patterns, providing a more interpretable and accurate measure of distinctiveness, which is invaluable for forensic examiners in prosecuting suspects.
SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning
Authors: Ren Wang, Haoliang Sun, Yuxiu Lin, Chuanhui Zuo, Yongshun Gong, Yilong Yin, Wenjia Meng
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Representation Learning, Sequential Decision Making

    Multi-view representation learning integrates multiple observable views of an entity into a unified representation to facilitate downstream tasks. Current methods predominantly focus on distinguishing compatible components across views, followed by a single-step parallel fusion process. However, this parallel fusion is static in essence, overlooking potential conflicts among views and compromising representation ability. To address this issue, this paper proposes a novel Sequential fusion framework for Multi-view Representation Learning, termed SeqMvRL. Specifically, we model multi-view fusion as a sequential decision-making problem and construct a pairwise integrator (PI) and a next-view selector (NVS), which represent the environment and agent in reinforcement learning, respectively. PI merges the current fused feature with the selected view, while NVS is introduced to determine which view to fuse subsequently. By adaptively selecting the next optimal view for fusion based on the current fusion state, SeqMvRL thereby effectively reduces conflicts and enhances unified representation quality. Additionally, an elaborate novel reward function encourages the model to prioritize views that enhance the discriminability of the fused features. Experimental results demonstrate that SeqMvRL outperforms parallel fusion approaches in classification and clustering tasks.
iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting
Authors: Tuo Cao, Fei Luo, Jiongming Qin, Yu Jiang, Yusen Wang, Chunxia Xiao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, 6D Pose Estimation, Data Augmentation, Pose Estimation

    Traditional methods in pose estimation often rely on precise 3D models or additional data such as depth and normals, limiting their generalization, especially when objects undergo large translations or rotations. We propose iG-6DoF, a novel model-free 6D pose estimation method iterative 3D Gaussian Splatting to estimate the pose of unseen objects. We first estimates an initial pose by leveraging multi-scale data augmentation and the rotation-equivariant features to create a better pose hypothesis from a set of candidates. Then, we propose an iterative 3DGS approach through iteratively rendering and comparing the rendered image with the input image to further progressively improve pose estimation accuracy. The proposed end-to-end network consists of an object detector, a multi-scale rotation-equivariant feature based initial pose estimator, and a coarse-to-fine pose refiner. Such combination allows our method to focus on the target object in a complex scene and deal with large movement and weak textures.Such framework allows to deal with large movement and weak texture and complex scene. We conduct extensive experiments on benchmark and real datasets. Our method achieves state-of-the-art results on the LINEMOD, OnePose-LowTexture, and GenMOP datasets, demonstrating its strong generalization to unseen objects and robustness across various scenes.
Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning
Authors: Maosen Zhao, Pengtao Chen, Chong Yu, Yan Wen, Xudong Tan, Tao Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Quantization

    Model quantization reduces the bit-width of weights and activations, improving memory efficiency and inference speed in diffusion models. However, achieving 4-bit quantization remains challenging. Existing methods, primarily based on integer quantization and post-training quantization fine-tuning, struggle with inconsistent performance. Inspired by the success of floating-point (FP) quantization in large language models, we explore low-bit FP quantization for diffusion models and identify key challenges: the failure of signed FP quantization to handle asymmetric activation distributions, the insufficient consideration of temporal complexity in the denoising process during fine-tuning, and the misalignment between fine-tuning loss and quantization error. To address these challenges, we propose the mixup-sign floating-point quantization (MSFP) framework, first introducing unsigned FP quantization in model quantization, along with timestep-aware LoRA (TALoRA) and denoising-factor loss alignment (DFA), which ensure precise and stable fine-tuning. Extensive experiments show that we are the first to achieve superior performance in 4-bit FP quantization for diffusion models, outperforming existing PTQ fine-tuning methods in 4-bit INT quantization.
GaPT-DAR: Category-level Garments Pose Tracking via Integrated 2D Deformation and 3D Reconstruction
Authors: Li Zhang, Mingliang Xu, Jianan Wang, Qiaojun Yu, Lixin Yang, Yonglu Li, Cewu Lu, Rujing Wang, Liu Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction, Pose Tracking

    Garments are common in daily life and are important for embodied intelligence community. Current category-level garments pose tracking works focus on predicting point-wise canonical correspondence and learning a shape deformation in point cloud sequences. In this paper, motivated by the 2D warping space and shape prior, we propose GaPT-DAR, a novel category-level Garments Pose Tracking framework with integrated 2D Deformation And 3D Reconstruction function, which fully utilize 3D-2D projection and 2D-3D reconstruction to transform the 3D point-wise learning into 2D warping deformation learning. Specifically, GaPT-DAR firstly builds a Voting-based Project module that learns the optimal 3D-2D projection plane for maintaining the maximum orthogonal entropy during point projection. Next, a Garments Deformation module is designed in 2D space to explicitly model the garments warping procedure with deformation parameters. Finally, we build a Depth Reconstruction module to recover the 2D images into 3D warp field. We provide extensive experiments on VR-Folding dataset to evaluate our GaPT-DAR and the results show obvious improvements on most of the metrics compared to state-of-the-arts ( GarmentNets and GarmentTracking). More details are available at https://sites.google.com/view/gapt-dar.
HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition
Authors: Zimo Wang, Cheng Wang, Taiki Yoshino, Sirui Tao, Ziyang Fu, Tzu-Mao Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Surface Reconstruction

    We propose a method, HotSpot, for optimizing neural signed distance functions. Existing losses, such as the eikonal loss, act as necessary but insufficient constraints and cannot guarantee that the recovered implicit function represents a true distance function, even if the output minimizes these losses almost everywhere. Furthermore, the eikonal loss suffers from stability issues in optimization. Finally, in conventional methods, regularization losses that penalize surface area distort the reconstructed signed distance function. We address these challenges by designing a loss function using the solution of a screened Poisson equation. Our loss, when minimized, provides an asymptotically sufficient condition to ensure the output converges to a true distance function. Our loss also leads to stable optimization and naturally penalizes large surface areas. We present theoretical analysis and experiments on both challenging 2D and 3D datasets and show that our method provides better surface reconstruction and a more accurate distance approximation.