pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Gaussian Mixture Model Splatting for Efficient and Part Controllable 3D Generation
Authors: Qitong Yang, Mingtao Feng, Zijie Wu, Weisheng Dong, Fangfang Wu, Yaonan Wang, Ajmal Mian
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Generation, Mamba

    3D content creation has achieved significant progress in terms of both quality and speed. Although current Gaussian Splatting-based methods can produce 3D objects within seconds, they are still limited by complex preprocessing or low controllability. In this paper, we introduce a novel framework designed to efficiently and controllably generate high-resolution 3D models from text promptsor image. Our key insights are three-fold: 1) Hierarchical Gaussian Mixture Model Splatting: We propose an hybrid hierarchical representation to extract fixed number of fine-grained Gaussians with multiscale details from textured object, also establish part-level representation of Gaussians primitives. 2) Mamba with adaptive tree topology: We present a diffusion mamba with tree-topology to adaptively generate Gaussians with disordered spatial structures, without the need for complex preprocessing and maintain linear complexity generation. 3) Controllable Generation: Building on the HGMM tree, we introduce a cascaded diffusion framework combining controllable implicit latent generation, which progressively generates condition-driven latents, and explicit splatting generation, which transforms latents into high-quality Gaussian primitives. Extensive experiments demonstrate the high fidelity and efficiency of our approach.
CoSER: Towards Consistent Dense Multiview Text-to-Image Generator for 3D Creation
Authors: Bonan Li, ZiCheng Zhang, Xingyi Yang, Xinchao Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Generation, Text to 3D

    Generating dense multiview images from text prompts is crucial for creating high-fidelity 3D assets. Nevertheless, existing methods struggle with space-view correspondences, resulting in sparse and low-quality outputs. In this paper, we introduce CoSER, a novel consistent dense Multiview Text-to-Image Generator for Text-to-3D, achieving both efficiency and quality by meticulously learning neighbor-view coherence and further alleviating ambiguity through the swift traversal of all views. For achieving neighbor-view consistency, each viewpoint densely interacts with adjacent viewpoints to perceive the global spatial structure, and aggregates information along motion paths explicitly defined by physical principles to refine details. To further enhance cross-view consistency and alleviate content drift, CoSER rapidly scan all views in spiral bidirectional manner to aware holistic information and then scores each point based on semantic material. Subsequently, we conduct weighted down-sampling along the spatial dimension based on scores, thereby facilitating prominent information fusion across all views with lightweight computation. Technically, the core module is built by integrating the attention mechanism with a selective state space model, exploiting the robust learning capabilities of the former and the low overhead of the latter. Extensive evaluation shows that CoSER is capable of producing dense, high-fidelity, content-consistent multiview images that can be flexibly integrated into various 3D generation models.
Exploring Historical Information for RGBE Visual Tracking with Mamba
Authors: Chuanyu Sun, Jiqing Zhang, Yang Wang, Huilin Ge, Qianchen Xia, BaoCai Yin, Xin Yang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Decoder, Long-range modeling, Mamba, Visual Tracking

    Combining the advantages of conventional and event cameras for robust visual tracing has drawn extensive interest. However, existing tracking approaches heavily engage in complex cross-modal fusion modules, leading to higher computational complexity and training challenges. Besides, these methods generally ignore the effective integration of historical information, which is crucial to grasping the change in the target's appearance and motion trends. Given the recent advancements in Mamba's long-range modeling and linear complexity, we explore its potential in addressing the above issues in RGBE tracking tasks. Specifically, we first propose an efficient fusion module based on Mamba, which utilizes a simple gate-based interaction scheme to achieve effective modality-selective fusion. This module can be seamlessly integrated into the encoding layer of prevalent Transformer-based backbones. Moreover, we further present a novel historical decoder that leverages Mamba's advanced long sequence modeling to effectively capture the target appearance changes with autoregressive queries. Extensive experiments show that our proposed approach achieves state-of-the-art performance on multiple challenging short-term and long-term RGBE benchmarks.  Besides, the effectiveness of each key Mamba-based component of our approach is evidenced by our thorough ablation study.
A3: Few-shot Prompt Learning of Unlearnable Examples with Cross-Modal Adversarial Feature Alignment
Authors: Xuan Wang, Xitong Gao, Dongping Liao, Tianrui Qin, Yu-liang Lu, Cheng-Zhong Xu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Few-Shot Learning, Prompt Learning

    In the age of pervasive machine learning applications, protecting digital content from unauthorized use has become a pressing concern. Unlearnable examples (UEs)--data modified with imperceptible perturbations to inhibit model training while preserving human usability--have emerged as a promising approach. However, existing UE methods assume unauthorized trainers have extensive exposure to UEs or that models are trained from scratch, which may not hold in practical scenarios, This paper investigates the effectiveness of UEs under the few-shot learning paradigm, pitching it against prompt learning (PL) models that leverage pretrained vision-language models (VLMs), like CLIP, capable of generalizing to new classes with minimal data. To address this, we introduce an adaptive UE framework to generate unlearnable examples that specifically target the PL process. In addition, we propose a novel UE countermeasure, A3, with cross-modal adversarial feature alignment, specifically designed to circumvent UEs under few-shot PL. Experimental evaluations on 7 datasets show that A3 outperforms existing PL methods, achieving up to 33% higher performance in learning from UEs. For example, in the scenario involving l_infinity-bounded EM perturbations, A3 has an average harmonic mean accuracy across 7 datasets of 82.43%, compared to CoCoOp's baseline of 65.47%. Our findings highlight the limitations of existing UEs against PL and lay the foundation for future data protection mechanisms.
Distinguish Then Exploit: Source-free Open Set Domain Adaptation via Weight Barcode Estimation and Sparse Label Assignment
Authors: Weiming Liu, Jun Dan, Fan Wang, Xinting Liao, Junhao Dong, Hua Yu, Shunjie Dong, Lianyong Qi
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Domain Adaptation, Privacy Preserving

    Nowadays, domain adaptation techniques have been widely investigated for knowledge sharing from labeled source domain to unlabeled target domain. However, target domain may include some data samples that belong to unknown categories in real-world scenarios. Moreover, the target domain cannot access the source data samples due to privacy-preserving restrictions. In this paper, we focus on the source-free open set domain adaptation problem which includes two main challenges, i.e.,how to distinguish known and unknown target samples and how to exploit useful source information to provide trustworthy pseudo labels for known target samples. Existing approaches that directly applying conventional domain alignment methods could lead to sample mismatch and misclassification in this scenario.To overcome these issues, we propose Distinguish Then Exploit model (DTE) with two components, i.e., weight barcode estimation and sparse label assignment. Weight barcode estimation first calculate marginal probability of target samples via partially unbalanced optimal transport then quantize barcode results to distinguish unknown target sample. Sparse label assignment utilizes sparse sample-label matching via proximal term to fully exploit useful source information. Our empirically study on several datasets shows that DTE outperforms the state-of-the-art models on tackling the source-free open set domain adaptation problem.
HSI-GPT: A General-Purpose Large Scene-Motion-Language Model for Human Scene Interaction
Authors: YuAn Wang, YaLi Li, Xiang Li, Shengjin Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Descriptive, Instruction Following, Language Modeling, Language Modelling, Motion Generation

    While flourishing developments have been witnessed in text-to-motion generation, synthesizing physically realistic, controllable, language-conditioned Human Scene Interactions (HSI) remains a relatively underexplored landscape. Current HSI methods naively rely on conditional Variational AutoEncoder (cVAE) and diffusion models. They are typically associated with limited modalities of control signals and task-specific frameworks design, leading to inflexible adaptation across various interaction scenarios and descriptive-unfaithful motions in diverse 3D physical environments. In this paper, we propose HSI-GPT, a General-Purpose Large Scene-Motion-Language Model that applies "next-token prediction" paradigm of Large Language Models to the HSI domain. HSI-GPT not only exhibits remarkable flexibility to accommodate diverse control signals (3D scenes, textual commands, key-frame poses, as well as scene affordances), but it seamlessly supports various HSI-related tasks (e.g., multi-modal controlled HSI generation, HSI understanding, and general motion completion in 3D scenes). First, HSI-GPT quantizes textual descriptions and human motions into discrete, LLM-interpretable tokens with multi-modal tokenizers. Inspired by multi-modal learning, we develop a recipe for aligning mixed-modality tokens into the shared embedding space of LLMs. These interaction tokens are then organized into unified instruction following prompts, allowing HSI-GPT to fine-tune on question-and-answer tasks. Extensive experiments and visualizations validate that our general-purpose HSI-GPT model delivers exceptional performance across multiple HSI-related tasks.
FreeTimeGS: Free Gaussian Primitives at Anytime Anywhere for Dynamic Scene Reconstruction
Authors: Yifan Wang, Peishan Yang, Zhen Xu, Jiaming Sun, Zhanhua Zhang, Yong Chen, Hujun Bao, Sida Peng, Xiaowei Zhou
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    This paper addresses the challenge of reconstructing dynamic 3D scenes with complex motions. Some recent works define 3D Gaussian primitives in the canonical space and use deformation fields to map canonical primitives to observation spaces, achieving real-time dynamic view synthesis. However, these methods often struggle to handle scenes with complex motions due to the difficulty of optimizing deformation fields. To overcome this problem, we propose FreeTimeGS, a novel 4D representation that allows Gaussian primitives to appear at arbitrary time and locations. In contrast to canonical Gaussian primitives, our representation possesses the strong flexibility, thus improving the ability to model dynamic 3D scenes. In addition, we endow each Gaussian primitive with an motion function, allowing it to move to neighboring regions over time, which reduces the temporal redundancy. Experiments results on several datasets show that the rendering quality of our method outperforms recent methods by a large margin. The code will be released for reproducibility.
Visual Representation Learning through Causal Intervention for Controllable Image Editing
Authors: Shanshan Huang, Haoxuan Li, Chunyuan Zheng, Lei Wang, Guorui Liao, Zhili Gong, Huayi Yang, Li Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Representation Learning

    A key challenge for controllable image editing is that visual attributes with semantic meanings are not always independent, resulting in spurious correlations in model training. However, most existing methods ignore such issues, leading to biased causal visual representation learning and unintended changes to unrelated regions or attributes in the edited images. To bridge this gap, we propose a diffusion-based causal visual representation learning framework called CIDiffuser to capture causal representations of visual attributes based on structural causal models to address the spurious correlation. Specifically, we first decompose the image representation into a high-level semantic representation for core attributes of the image and a low-level stochastic representation for other random or less structured aspects, with the former extracted by a semantic encoder and the latter derived via a stochastic encoder. We then introduce a causal effect learning module to capture the direct causal effect, that is, the difference of potential outcomes before and after intervening on the visual attributes.  In addition, a diffusion-based learning strategy is designed to optimize the representation learning process. Empirical evaluations on two benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, enabling highly controllable image editing by modifying learned visual representations.
Continuous Adverse Weather Removal via Degradation-Aware Distillation
Authors: Xin Lu, Jie Xiao, Yurui Zhu, Xueyang Fu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Incremental Learning

    All-in-one models for adverse weather removal aim to process various degraded images using a single set of parameters, making them ideal for real-world scenarios. However, they encounter two main challenges: catastrophic forgetting and limited degradation awareness. The former causes the model to lose knowledge of previously learned scenarios, reducing its overall effectiveness. While the later hampers the model's ability to accurately identify and respond to specific types of degradation, limiting its performance across diverse adverse weather conditions. To address these issues, we introduce the Incremental Learning Adverse Weather Removal (ILAWR) framework, which uses a novel degradation-aware distillation strategy for continuous weather removal. Specifically, we first design a degradation-aware module that utilizes Fourier priors to capture a broad range of degradation features, effectively mitigating catastrophic forgetting in low-level visual tasks. Then, we implement multilateral distillation, which combines knowledge from multiple teacher models using an importance-guided aggregation approach. This enables the model to balance adaptation to new degradation types with the preservation of background details. Extensive experiments on both synthetic and real-world datasets confirm that ILAWR outperforms existing models across multiple benchmarks, proving its effectiveness in continuous adverse weather removal.
Enduring, Efficient and Robust Trajectory Prediction Attack in Autonomous Driving via Optimization-Driven Multi-Frame Perturbation Framework
Authors: Yi Yu, Weizhen Han, Libing Wu, Bingyi Liu, Enshu Wang, Zhuangzhuang Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Driving, Trajectory Prediction

    Trajectory prediction plays a crucial role in autonomous driving systems, and exploring its vulnerability has garnered widespread attention. However, existing trajectory prediction attack methods often rely on single-point attacks to make efficient perturbations. This limits their applications in real-world scenarios due to the transient nature of single-point attacks, their susceptibility to filtration, and the uncertainty regarding the deployment environment. To address these challenges, this paper proposes a novel LiDAR-induced attack framework to impose multi-frame attacks by optimization-driven adversarial location search, achieving endurance, efficiency, and robustness. This framework strategically places objects near the adversarial vehicle to implement an attack and introduces three key innovations. First, successive state perturbations are generated using a multi-frame single-point attack strategy, effectively misleading trajectory predictions over extended time horizons. Second, we efficiently optimize adversarial objects' locations through three specialized loss functions to achieve desired perturbations. Lastly, we improve robustness by treating the adversarial object as a point without size constraints during the location search phase and reduce dependence on both the specific attack point and the adversarial object's properties. Extensive experiments confirm the superior performance and robustness of our framework.
Exploring Timeline Control for Facial Motion Generation
Authors: Yifeng Ma, Jinwei Qi, Chaonan Ji, Peng Zhang, Bang Zhang, Zhidong Deng, Liefeng Bo
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Motion Generation

    This paper introduces a new control signal for facial motion generation: timeline control. Compared to audio and text signals, timelines provide more fine-grained control, such as generating specific facial motions with precise timing. Users can specify a multi-track timeline of facial actions arranged in temporal intervals, allowing precise control over the timing of each action. To model the timeline control capability, We first annotate the time intervals of facial actions in natural facial motion sequences at a frame-level granularity. This process is facilitated by Toeplitz Inverse Covariance-based Clustering to minimize human labor. Based on the annotations, we propose a diffusion-based generation model capable of generating facial motions that are natural and accurately aligned with input timelines. Our method supports text-guided motion generation by using ChatGPT to convert text into timelines. Experimental results show that our method can annotate facial action intervals with satisfactory accuracy, and produces natural facial motions accurately aligned with timelines.
Ges3ViG : Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference Understanding
Authors: Atharv Mahesh Mane, Dulanga Weerakoon, Vigneshwaran Subbaraju, Sougata Sen, Sanjay E. Sarma, Archan Misra
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D visual grounding, Data Augmentation, Visual Grounding

    3-Dimensional Embodied Reference Understanding (3DERU) combines a language description and an accompanying pointing gesture to identify the most relevant target object in a 3D scene. Although prior work has explored pure language-based 3D grounding, there has been limited exploration of 3D-ERU, which also incorporates human pointing gestures. To address this gap, we introduce a data augmentation framework-Imputer, and use it to curate a new benchmark dataset-ImputeRefer for 3D-ERU, by incorporating human pointing gestures into existing 3D scene datasets that only contain language instructions. We also propose Ges3ViG, a novel model for 3D-ERU that achieves  30% improvement in accuracy as compared to other 3DERU models and  9% compared to other purely language-based 3D grounding models. Our code and dataset are available at https://github.com/AtharvMane/Ges3ViG.
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization
Authors: Shilhora Akshay, Niveditha Lakshmi Narasimhan, Jacob George, Vineeth N Balasubramanian
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

    Anomaly detection and localization remain pivotal challenges in computer vision, with applications ranging from industrial inspection to medical diagnostics. While current supervised methods offer high precision, they are often impractical due to the scarcity of annotated data and the infrequent occurrence of anomalies. Recent advancements in unsupervised approaches, particularly reconstruction-based methods, have addressed these issues by training models exclusively on normal data, enabling them to identify anomalies during inference. However, these methods frequently rely on auxiliary networks or specialized adaptations, which can limit their robustness and practicality. This work introduces the Latent Anomaly Schrodinger Bridge (LASB), a unified unsupervised anomaly detection model that operates entirely in the latent space without requiring additional networks or custom modifications. LASB transforms anomaly images into normal images by preserving structural integrity across varying anomaly classes, lighting, and pose conditions, making it highly robust and versatile. Unlike previous methods, LASB does not focus solely on reconstructing anomaly features, but emphasizes anomaly transformation, achieving smooth anomaly-to-normal image conversions. Our method achieves state-of-the-art performance on both the MVTec-AD and VisA datasets, excelling in detection and localization tasks.
MAD: Memory-Augmented Detection of 3D Objects
Authors: Ben Agro, Sergio Casas, Patrick Wang, Thomas Gilles, Raquel Urtasun
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Object

    To perceive, humans use memory to fill in gaps caused by our limited visibility, whether due to occlusion or our narrow field of view. However, most 3D object detectors are limited to using sensor evidence from a short temporal window (0.1s-0.3s). In this work, we present a simple and effective add-on for enhancing any existing 3D object detector with long-term memory regardless of its sensor modality (e.g., LiDAR, camera) and network architecture. We propose a model to effectively align and fuse object proposals from a detector with object proposals from a memory bank of past predictions, exploiting trajectory forecasts to align proposals across time. We propose a novel schedule to train our model on temporal data that balances data diversity and the gap between training and inference. By applying our method to existing LiDAR and camera-based detectors on the Waymo Open Dataset (WOD) and Argoverse 2 Sensor (AV2) dataset, we demonstrate significant improvements in detection performance (+2.5 to +7.6 AP points). Our method attains the best performance on the WOD 3D detection leaderboard among online methods (excluding ensembles or test-time augmentation).
EDM: Equirectangular Projection-Oriented Dense Kernelized Feature Matching
Authors: Dongki Jung, Jaehoon Choi, Yonghan Lee, Somi Jeong, Taejae Lee, Dinesh Manocha, Suyong Yeon
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: ERP

    We introduce the first learning-based dense matching algorithm, termed Equirectangular Projection-Oriented Dense Kernelized Feature Matching (EDM), specifically designed for omnidirectional images. Equirectangular projection (ERP) images, with their large fields of view, are particularly suited for dense matching techniques that aim to establish comprehensive correspondences across images. However, ERP images are subject to significant distortions, which we address by leveraging the spherical camera model and geodesic flow refinement in the dense matching method. To further mitigate these distortions, we propose spherical positional embeddings based on 3D Cartesian coordinates of the feature grid. Additionally, our method incorporates bidirectional transformations between spherical and Cartesian coordinate systems during refinement, utilizing a unit sphere to improve matching performance. We demonstrate that our proposed method achieves notable performance enhancements, with improvements of +26.72 and +42.62 in AUC@5deg on the Matterport3D and Stanford2D3D datasets. Project page: https://jdk9405.github.io/EDM