pwc-archive/papers-with-abstracts huggingface dataset:
CHROME: Clothed Human Reconstruction with Occlusion-Resilience and Multiview-Consistency from a Single Image
Authors: Arindam Dutta, Meng Zheng, Zhongpai Gao, Benjamin Planche, Anwesha Choudhuri, Terrence Chen, Amit K. Roy-Chowdhury, Ziyan Wu
Date: 2025-03-19 00:00:00
Tasks: 3D Human Reconstruction, 3D Reconstruction, Novel View Synthesis

Reconstructing clothed humans from a single image is a fundamental task in computer vision with wide-ranging applications. Although existing monocular clothed human reconstruction solutions have shown promising results, they often rely on the assumption that the human subject is in an occlusion-free environment. Thus, when encountering in-the-wild occluded images, these algorithms produce multiview inconsistent and fragmented reconstructions. Additionally, most algorithms for monocular 3D human reconstruction leverage geometric priors such as SMPL annotations for training and inference, which are extremely challenging to acquire in real-world applications. To address these limitations, we propose CHROME: Clothed Human Reconstruction with Occlusion-Resilience and Multiview-ConsistEncy from a Single Image, a novel pipeline designed to reconstruct occlusion-resilient 3D humans with multiview consistency from a single occluded image, without requiring either ground-truth geometric prior annotations or 3D supervision. Specifically, CHROME leverages a multiview diffusion model to first synthesize occlusion-free human images from the occluded input, compatible with off-the-shelf pose control to explicitly enforce cross-view consistency during synthesis. A 3D reconstruction model is then trained to predict a set of 3D Gaussians conditioned on both the occluded input and synthesized views, aligning cross-view details to produce a cohesive and accurate 3D representation. CHROME achieves significant improvements in terms of both novel view synthesis (upto 3 db PSNR) and geometric reconstruction under challenging conditions.
High Temporal Consistency through Semantic Similarity Propagation in Semi-Supervised Video Semantic Segmentation for Autonomous Flight
Authors: Cédric Vincent, Taehyoung Kim, Henri Meeß
Date: 2025-03-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Knowledge Distillation, Segmentation, Semantic Segmentation, Semantic Similarity, Semantic Textual Similarity, Video Semantic Segmentation

Semantic segmentation from RGB cameras is essential to the perception of autonomous flying vehicles. The stability of predictions through the captured videos is paramount to their reliability and, by extension, to the trustworthiness of the agents. In this paper, we propose a lightweight video semantic segmentation approach-suited to onboard real-time inference-achieving high temporal consistency on aerial data through Semantic Similarity Propagation across frames. SSP temporally propagates the predictions of an efficient image segmentation model with global registration alignment to compensate for camera movements. It combines the current estimation and the prior prediction with linear interpolation using weights computed from the features similarities of the two frames. Because data availability is a challenge in this domain, we propose a consistency-aware Knowledge Distillation training procedure for sparsely labeled datasets with few annotations. Using a large image segmentation model as a teacher to train the efficient SSP, we leverage the strong correlations between labeled and unlabeled frames in the same training videos to obtain high-quality supervision on all frames. KD-SSP obtains a significant temporal consistency increase over the base image segmentation model of 12.5% and 6.7% TC on UAVid and RuralScapes respectively, with higher accuracy and comparable inference speed. On these aerial datasets, KD-SSP provides a superior segmentation quality and inference speed trade-off than other video methods proposed for general applications and shows considerably higher consistency. The code will be made publicly available upon acceptance.
The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation
Authors: Benidir Yanis, Gonthier Nicolas, Mallet Clement
Date: 2025-03-19 00:00:00
Tasks: Change Detection, Earth Observation

Bi-temporal change detection at scale based on Very High Resolution (VHR) images is crucial for Earth monitoring. This remains poorly addressed so far: methods either require large volumes of annotated data (semantic case), or are limited to restricted datasets (binary set-ups). Most approaches do not exhibit the versatility required for temporal and spatial adaptation: simplicity in architecture design and pretraining on realistic and comprehensive datasets. Synthetic datasets are the key solution but still fail to handle complex and diverse scenes. In this paper, we present HySCDG a generative pipeline for creating a large hybrid semantic change detection dataset that contains both real VHR images and inpainted ones, along with land cover semantic map at both dates and the change map. Being semantically and spatially guided, HySCDG generates realistic images, leading to a comprehensive and hybrid transfer-proof dataset FSC-180k. We evaluate FSC-180k on five change detection cases (binary and semantic), from zero-shot to mixed and sequential training, and also under low data regime training. Experiments demonstrate that pretraining on our hybrid dataset leads to a significant performance boost, outperforming SyntheWorld, a fully synthetic dataset, in every configuration. All codes, models, and data are available here: $\href{https://yb23.github.io/projects/cywd/}{https://yb23.github.io/projects/cywd/}$.
Multi-focal Conditioned Latent Diffusion for Person Image Synthesis
Authors: Jiaqi Liu, Jichao Zahng, Paolo Rota, Nicu Sebe
Date: 2025-03-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation

The Latent Diffusion Model (LDM) has demonstrated strong capabilities in high-resolution image generation and has been widely employed for Pose-Guided Person Image Synthesis (PGPIS), yielding promising results. However, the compression process of LDM often results in the deterioration of details, particularly in sensitive areas such as facial features and clothing textures. In this paper, we propose a Multi-focal Conditioned Latent Diffusion (MCLD) method to address these limitations by conditioning the model on disentangled, pose-invariant features from these sensitive regions. Our approach utilizes a multi-focal condition aggregation module, which effectively integrates facial identity and texture-specific information, enhancing the model's ability to produce appearance realistic and identity-consistent images. Our method demonstrates consistent identity and appearance generation on the DeepFashion dataset and enables flexible person image editing due to its generation consistency. The code is available at https://github.com/jqliu09/mcld.
Technical Report for the 5th CLVision Challenge at CVPR: Addressing the Class-Incremental with Repetition using Unlabeled Data -- 4th Place Solution
Authors: Panagiota Moraiti, Efstathios Karypidis
Date: 2025-03-19 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning, Knowledge Distillation

This paper outlines our approach to the 5th CLVision challenge at CVPR, which addresses the Class-Incremental with Repetition (CIR) scenario. In contrast to traditional class incremental learning, this novel setting introduces unique challenges and research opportunities, particularly through the integration of unlabeled data into the training process. In the CIR scenario, encountered classes may reappear in later learning experiences, and each experience may involve only a subset of the overall class distribution. Additionally, the unlabeled data provided during training may include instances of unseen classes, or irrelevant classes which should be ignored. Our approach focuses on retaining previously learned knowledge by utilizing knowledge distillation and pseudo-labeling techniques. The key characteristic of our method is the exploitation of unlabeled data during training, in order to maintain optimal performance on instances of previously encountered categories and reduce the detrimental effects of catastrophic forgetting. Our method achieves an average accuracy of 16.68\% during the pre-selection phase and 21.19% during the final evaluation phase, outperforming the baseline accuracy of 9.39%. We provide the implementation code at https://github.com/panagiotamoraiti/continual-learning-challenge-2024 .
SPNeRF: Open Vocabulary 3D Neural Scene Segmentation with Superpoints
Authors: Weiwen Hu, Niccolò Parodi, Marcus Zepp, Ingo Feldmann, Oliver Schreer, Peter Eisert
Date: 2025-03-19 00:00:00
Tasks: NeRF, Scene Segmentation, Segmentation

Open-vocabulary segmentation, powered by large visual-language models like CLIP, has expanded 2D segmentation capabilities beyond fixed classes predefined by the dataset, enabling zero-shot understanding across diverse scenes. Extending these capabilities to 3D segmentation introduces challenges, as CLIP's image-based embeddings often lack the geometric detail necessary for 3D scene segmentation. Recent methods tend to address this by introducing additional segmentation models or replacing CLIP with variations trained on segmentation data, which lead to redundancy or loss on CLIP's general language capabilities. To overcome this limitation, we introduce SPNeRF, a NeRF based zero-shot 3D segmentation approach that leverages geometric priors. We integrate geometric primitives derived from the 3D scene into NeRF training to produce primitive-wise CLIP features, avoiding the ambiguity of point-wise features. Additionally, we propose a primitive-based merging mechanism enhanced with affinity scores. Without relying on additional segmentation models, our method further explores CLIP's capability for 3D segmentation and achieves notable improvements over original LERF.
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification
Authors: Yuqing Zhang, Qi Han, Ligeng Wang, Kai Cheng, Bo wang, Kun Zhan
Date: 2025-03-19 00:00:00
Tasks: Contrastive Learning, Hyperspectral Image Classification, image-classification, Image Classification

Most existing graph-based semi-supervised hyperspectral image classification methods rely on superpixel partitioning techniques. However, they suffer from misclassification of certain pixels due to inaccuracies in superpixel boundaries, \ie, the initial inaccuracies in superpixel partitioning limit overall classification performance. In this paper, we propose a novel graph-weighted contrastive learning approach that avoids the use of superpixel partitioning and directly employs neural networks to learn hyperspectral image representation. Furthermore, while many approaches require all graph nodes to be available during training, our approach supports mini-batch training by processing only a subset of nodes at a time, reducing computational complexity and improving generalization to unseen nodes. Experimental results on three widely-used datasets demonstrate the effectiveness of the proposed approach compared to baselines relying on superpixel partitioning.
Uncertainty-Aware Diffusion Guided Refinement of 3D Scenes
Authors: Sarosij Bose, Arindam Dutta, Sayak Nag, Junge Zhang, Jiachen Li, Konstantinos Karydis, Amit K. Roy Chowdhury
Date: 2025-03-19 00:00:00
Tasks: 3D Reconstruction, Image to 3D, Novel View Synthesis, Style Transfer, Uncertainty Quantification

Reconstructing 3D scenes from a single image is a fundamentally ill-posed task due to the severely under-constrained nature of the problem. Consequently, when the scene is rendered from novel camera views, existing single image to 3D reconstruction methods render incoherent and blurry views. This problem is exacerbated when the unseen regions are far away from the input camera. In this work, we address these inherent limitations in existing single image-to-3D scene feedforward networks. To alleviate the poor performance due to insufficient information beyond the input image's view, we leverage a strong generative prior in the form of a pre-trained latent video diffusion model, for iterative refinement of a coarse scene represented by optimizable Gaussian parameters. To ensure that the style and texture of the generated images align with that of the input image, we incorporate on-the-fly Fourier-style transfer between the generated images and the input image. Additionally, we design a semantic uncertainty quantification module that calculates the per-pixel entropy and yields uncertainty maps used to guide the refinement process from the most confident pixels while discarding the remaining highly uncertain ones. We conduct extensive experiments on real-world scene datasets, including in-domain RealEstate-10K and out-of-domain KITTI-v2, showing that our approach can provide more realistic and high-fidelity novel view synthesis results compared to existing state-of-the-art methods.
GraPLUS: Graph-based Placement Using Semantics for Image Composition
Authors: Mir Mohammad Khaleghi, Mehran Safayani, Abdolreza Mirzaei
Date: 2025-03-20 00:00:00
Tasks: Object

We present GraPLUS (Graph-based Placement Using Semantics), a novel framework for plausible object placement in images that leverages scene graphs and large language models. Our approach uniquely combines graph-structured scene representation with semantic understanding to determine contextually appropriate object positions. The framework employs GPT-2 to transform categorical node and edge labels into rich semantic embeddings that capture both definitional characteristics and typical spatial contexts, enabling nuanced understanding of object relationships and placement patterns. GraPLUS achieves placement accuracy of 92.1% and an FID score of 28.83 on the OPA dataset, outperforming state-of-the-art methods by 8.1% while maintaining competitive visual quality. In human evaluation studies involving 964 samples assessed by 19 participants, our method was preferred in 52.1% of cases, significantly outperforming previous approaches. The framework's key innovations include: (i) leveraging pre-trained scene graph models that transfer knowledge from other domains, (ii) edge-aware graph neural networks that process scene semantics through structured relationships, (iii) a cross-modal attention mechanism that aligns categorical embeddings with enhanced scene features, and (iv) a multiobjective training strategy incorporating semantic consistency constraints.
OffsetOPT: Explicit Surface Reconstruction without Normals
Authors: Huan Lei
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Surface Reconstruction

Neural surface reconstruction has been dominated by implicit representations with marching cubes for explicit surface extraction. However, those methods typically require high-quality normals for accurate reconstruction. We propose OffsetOPT, a method that reconstructs explicit surfaces directly from 3D point clouds and eliminates the need for point normals. The approach comprises two stages: first, we train a neural network to predict surface triangles based on local point geometry, given uniformly distributed training point clouds. Next, we apply the frozen network to reconstruct surfaces from unseen point clouds by optimizing a per-point offset to maximize the accuracy of triangle predictions. Compared to state-of-the-art methods, OffsetOPT not only excels at reconstructing overall surfaces but also significantly preserves sharp surface features. We demonstrate its accuracy on popular benchmarks, including small-scale shapes and large-scale open surfaces.
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Authors: Parham Saremi, Amar Kumar, Mohammed Mohammed, Zahra Tehraninasab, Tal Arbel
Date: 2025-03-20 00:00:00
Tasks: Image Generation, Medical Image Generation, Reinforcement Learning (RL)

Vision-Language Foundation Models (VLFM) have shown a tremendous increase in performance in terms of generating high-resolution, photorealistic natural images. While VLFMs show a rich understanding of semantic content across modalities, they often struggle with fine-grained alignment tasks that require precise correspondence between image regions and textual descriptions a limitation in medical imaging, where accurate localization and detection of clinical features are essential for diagnosis and analysis. To address this issue, we propose a multi-stage architecture where a pre-trained VLFM provides a cursory semantic understanding, while a reinforcement learning (RL) algorithm refines the alignment through an iterative process that optimizes for understanding semantic context. The reward signal is designed to align the semantic information of the text with synthesized images. We demonstrate the effectiveness of our method on a medical imaging skin dataset where the generated images exhibit improved generation quality and alignment with prompt over the fine-tuned Stable Diffusion. We also show that the synthesized samples could be used to improve disease classifier performance for underrepresented subgroups through augmentation.
Frequency Enhancement for Image Demosaicking
Authors: Jingyun Liu, Daiqin Yang, Zhenzhong Chen
Date: 2025-03-20 00:00:00
Tasks: Demosaicking

Recovering high-frequency textures in image demosaicking remains a challenging issue. While existing methods introduced elaborate spatial learning methods, they still exhibit limited performance. To address this issue, a frequency enhancement approach is proposed. Based on the frequency analysis of color filter array (CFA)/demosaicked/ground truth images, we propose Dual-path Frequency Enhancement Network (DFENet), which reconstructs RGB images in a divide-and-conquer manner through fourier-domain frequency selection. In DFENet, two frequency selectors are employed, each selecting a set of frequency components for processing along separate paths. One path focuses on generating missing information through detail refinement in spatial domain, while the other aims at suppressing undesirable frequencies with the guidance of CFA images in frequency domain. Multi-level frequency supervision with a stagewise training strategy is employed to further improve the reconstruction performance. With these designs, the proposed DFENet outperforms other state-of-the-art algorithms on different datasets and demonstrates significant advantages on hard cases. Moreover, to better assess algorithms' ability to reconstruct high-frequency textures, a new dataset, LineSet37, is contributed, which consists of 37 artificially designed and generated images. These images feature complex line patterns and are prone to severe visual artifacts like color moir\'e after demosaicking. Experiments on LineSet37 offer a more targeted evaluation of performance on challenging cases. The code and dataset are available at https://github.com/VelvetReverie/DFENet-demosaicking.
A Vision Centric Remote Sensing Benchmark
Authors: Abduljaleel Adejumo, Faegheh Yeganli, Clifford Broni-Bediako, Aoran Xiao, Naoto Yokoya, Mennatullah Siam
Date: 2025-03-20 00:00:00
Tasks: Question Answering, Representation Learning, Spatial Reasoning, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored. Unlike natural images, RS imagery presents unique challenges that current MLLMs struggle to handle, particularly in visual grounding and spatial reasoning. This study investigates the limitations of CLIP-based MLLMs in RS, highlighting their failure to differentiate visually distinct yet semantically similar RS images. To address this, we introduce a remote sensing multimodal visual patterns (RSMMVP) benchmark. It is designed to evaluate MLLMs in RS tasks by identifying the CLIP-blind pairs, where CLIP-based models incorrectly assign high similarity scores to visually distinct RS images. Through a visual question answering (VQA) evaluation, we analyze the performance of state-of-the-art MLLMs, revealing significant limitations in RS specific representation learning. The results provide valuable insights into the weaknesses of CLIP-based visual encoding and offer a foundation for future research to develop more effective MLLMs tailored for remote sensing applications.
EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation
Authors: Zihao Zhang, Haoran Chen, Haoyu Zhao, Guansong Lu, Yanwei Fu, Hang Xu, Zuxuan Wu
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Optical Flow Estimation, Video Frame Interpolation

Handling complex or nonlinear motion patterns has long posed challenges for video frame interpolation. Although recent advances in diffusion-based methods offer improvements over traditional optical flow-based approaches, they still struggle to generate sharp, temporally consistent frames in scenarios with large motion. To address this limitation, we introduce EDEN, an Enhanced Diffusion for high-quality large-motion vidEo frame iNterpolation. Our approach first utilizes a transformer-based tokenizer to produce refined latent representations of the intermediate frames for diffusion models. We then enhance the diffusion transformer with temporal attention across the process and incorporate a start-end frame difference embedding to guide the generation of dynamic motion. Extensive experiments demonstrate that EDEN achieves state-of-the-art results across popular benchmarks, including nearly a 10% LPIPS reduction on DAVIS and SNU-FILM, and an 8% improvement on DAIN-HD.
BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting
Authors: Yiren Lu, Yunlai Zhou, Disheng Liu, Tuo Liang, Yu Yin
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, Deblurring

3D Gaussian Splatting (3DGS) has shown remarkable potential for static scene reconstruction, and recent advancements have extended its application to dynamic scenes. However, the quality of reconstructions depends heavily on high-quality input images and precise camera poses, which are not that trivial to fulfill in real-world scenarios. Capturing dynamic scenes with handheld monocular cameras, for instance, typically involves simultaneous movement of both the camera and objects within a single exposure. This combined motion frequently results in image blur that existing methods cannot adequately handle. To address these challenges, we introduce BARD-GS, a novel approach for robust dynamic scene reconstruction that effectively handles blurry inputs and imprecise camera poses. Our method comprises two main components: 1) camera motion deblurring and 2) object motion deblurring. By explicitly decomposing motion blur into camera motion blur and object motion blur and modeling them separately, we achieve significantly improved rendering results in dynamic regions. In addition, we collect a real-world motion blur dataset of dynamic scenes to evaluate our approach. Extensive experiments demonstrate that BARD-GS effectively reconstructs high-quality dynamic scenes under realistic conditions, significantly outperforming existing methods.