GenAI4ELab/papercli-papers huggingface dataset:
Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction
Authors: Rui Qian; Shuangrui Ding; Xiaoyi Dong; Pan Zhang; Yuhang Zang; Yuhang Cao; Dahua Lin; Jiaqi Wang
Venue: CVPR 2025 (main)

Abstract: Active Real-time interaction with video LLMs introduces a new paradigm for human-computer interaction, where the model not only understands user intent but also responds while continuously processing streaming video on the fly. Unlike offline video LLMs, which analyze the entire video before answering questions, active real-time interaction requires three capabilities: 1) Perception: real-time video monitoring and interaction capturing. 2) Decision: raising proactive interaction in proper situations, 3) Reaction: continuous interaction with users. However, inherent conflicts exist among the desired capabilities. The Decision and Reaction require a contrary Perception scale and grain, and the autoregressive decoding blocks the real-time Perception and Decision during the Reaction. To unify the conflicted capabilities within a harmonious system, we present Dispider, a solution built on a Disentangled Perception, Decision, and Reaction framework. Dispider features a lightweight Proactive Streaming Video Processing module that tracks the video stream and identifies optimal moments for interaction. Once the interaction is triggered, an asynchronous Precise Interaction module provides detailed responses, while the processing module continues to monitor the video in the meantime. Our disentangled and asynchronous design ensures timely, contextually accurate, and computationally efficient responses, making Dispider ideal for active real-time interaction for long-duration video streams. Experiments prove that Dispider outperforms existing methods not only in its superior understanding of video content in conventional video QA settings, but also in proactive response capability and temporal awareness under the streaming setting.
NTClick: Achieving Precise Interactive Segmentation With Noise-tolerant Clicks
Authors: Chenyi Zhang; Ting Liu; Xiaochao Qu; Luoqi Liu; Yao Zhao; Yunchao Wei
Venue: CVPR 2025 (main)

Abstract: 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.
Show and Segment: Universal Medical Image Segmentation via In-Context Learning
Authors: Yunhe Gao; Di Liu; Zhuowei Li; Yunsheng Li; Dongdong Chen; Mu Zhou; Dimitris N. Metaxas
Venue: CVPR 2025 (main)

Abstract: Medical image segmentation remains challenging due to the vast diversity of anatomical structures, imaging modalities, and segmentation tasks. While deep learning has made significant advances, current approaches struggle to generalize as they require task-specific training or fine-tuning on unseen classes. We present Iris, a novel In-context Reference Image guided Segmentation framework that enables flexible adaptation to novel tasks through the use of reference examples without fine-tuning. At its core, Iris features a lightweight context task encoding module that distills task-specific information from reference context image-label pairs. This rich context embedding information is used to guide the segmentation of target objects. Given a decoupled architecture on 3D data processing, Iris supports diverse inference strategies including one-shot inference, context example ensemble, object-level context example retrieval, and in-context tuning. Through comprehensive evaluation across twelve datasets, we demonstrate that Iris performs strongly compared to specialized supervised models on in-distribution tasks. On seven held-out dataset, Iris shows superior generalization to out-of-distribution data and unseen classes. Further, Iris's task encoding module can automatically discover anatomical relationships across datasets and modalities, offering insights into cross-modality medical objects without explicit anatomical supervision.
MVGenMaster: Scaling Multi-View Generation from Any Image via 3D Priors Enhanced Diffusion Model
Authors: Chenjie Cao; Chaohui Yu; Shang Liu; Fan Wang; Xiangyang Xue; Yanwei Fu
Venue: CVPR 2025 (main)

Abstract: We introduce MVGenMaster, a multi-view diffusion model enhanced with 3D priors to address versatile Novel View Synthesis (NVS) tasks. MVGenMaster leverages 3D priors that are warped using metric depth and camera poses, significantly enhancing both generalization and 3D consistency in NVS. Our model features a simple yet effective pipeline that can generate up to 100 novel views conditioned on variable reference views and camera poses with a single forward process. Additionally, we have developed a comprehensive large-scale multi-view image dataset called MvD-1M, comprising up to 1.6 million scenes, equipped with well-aligned metric depth to train MVGenMaster. Moreover, we present several training and model modifications to strengthen the model with scaled-up datasets. Extensive evaluations across in- and out-of-domain benchmarks demonstrate the effectiveness of our proposed method and data formulation.
CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images
Authors: Cheng Chen; Jiacheng Wei; Tianrun Chen; Chi Zhang; Xiaofeng Yang; Shangzhan Zhang; Bingchen Yang; Chuan-Sheng Foo; Guosheng Lin; Qixing Huang; Fayao Liu
Venue: CVPR 2025 (main)

Abstract: Creating CAD digital twins from the physical world is crucial for manufacturing, design, and simulation. However, current methods typically rely on costly 3D scanning with labor-intensive post-processing. To provide a user-friendly design process, we explore the problem of reverse engineering from unconstrained real-world CAD images that can be easily captured by users of all experiences. However, the scarcity of real-world CAD data poses challenges in directly training such models. To tackle these challenges, we propose CADCrafter, an image-to-parametric CAD model generation framework that trains solely on synthetic textureless CAD data while testing on real-world images. To bridge the significant representation disparity between images and parametric CAD models, we introduce a geometry encoder to accurately capture diverse geometric features. Moreover, the texture-invariant properties of the geometric features can also facilitate the generalization to real-world scenarios. Since compiling CAD parameter sequences into explicit CAD models is a non-differentiable process, the network training inherently lacks explicit geometric supervision. To impose geometric validity constraints, we employ direct preference optimization (DPO) to fine-tune our model with the automatic code checker feedback on CAD sequence quality. Furthermore, we collected a real-world dataset, comprised of multi-view images and corresponding CAD command sequence pairs, to evaluate our method. Experimental results demonstrate that our approach can robustly handle real unconstrained CAD images, and even generalize to unseen general objects.
Bayesian Test-Time Adaptation for Vision-Language Models
Authors: Lihua Zhou; Mao Ye; Shuaifeng Li; Nianxin Li; Xiatian Zhu; Lei Deng; Hongbin Liu; Zhen Lei
Venue: CVPR 2025 (main)

Abstract: Test-time adaptation with pre-trained vision-language models, such as CLIP, aims to adapt the model to new, potentially out-of-distribution test data.  Existing methods calculate the similarity between visual embedding and learnable class embeddings, which are initialized by text embeddings, for zero-shot image classification. In this work, we first analyze this process based on Bayes theorem, and observe that the core factors influencing the final prediction are the likelihood and the prior. However, existing methods essentially focus on adapting class embeddings to adapt likelihood, but they often ignore the importance of prior. To address this gap, we propose a novel approach, Bayesian Class Adaptation (BCA), which in addition to continuously updating class embeddings to adapt likelihood, also uses the posterior of incoming samples to continuously update the prior for each class embedding. This dual updating mechanism allows the model to better adapt to distribution shifts and achieve higher prediction accuracy. Our method not only surpasses existing approaches in terms of performance metrics but also maintains superior inference rates and memory usage, making it highly efficient and practical for real-world applications.
Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation
Authors: Hadi Alzayer; Philipp Henzler; Jonathan T. Barron; Jia-Bin Huang; Pratul P. Srinivasan; Dor Verbin
Venue: CVPR 2025 (main)

Abstract: Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for more specular objects whose appearance strongly depends on the viewing direction. Some prior approaches model appearance variation across images using a per-image embedding vector, while others use physically-based rendering to recover the materials and per-image illumination. Such approaches fail at faithfully recovering view-dependent appearance given significant variation in input illumination and tend to produce mostly diffuse results. We present an approach that reconstructs objects from images taken under different illuminations by first relighting the images under a single reference illumination with a multiview relighting diffusion model and then reconstructing the object's geometry and appearance with a radiance field architecture that is robust to the small remaining inconsistencies among the relit images. We validate our proposed approach on both simulated and real datasets and demonstrate that it greatly outperforms existing techniques at reconstructing high-fidelity appearance from images taken under extreme illumination variation. Moreover, our approach is particularly effective at recovering view-dependent "shiny" appearance which cannot be reconstructed by prior methods.
Causal Composition Diffusion Model for Closed-loop Traffic Generation
Authors: Haohong Lin; Xin Huang; Tung Phan; David Hayden; Huan Zhang; Ding Zhao; Siddhartha Srinivasa; Eric Wolff; Hongge Chen
Venue: CVPR 2025 (main)

Abstract: Simulation is critical for safety evaluation in autonomous driving, particularly in capturing complex interactive behaviors. However, generating **realistic** and **controllable** traffic scenarios in long-tail situations remains a significant challenge. Existing generative models suffer from the conflicting objective between user-defined controllability and realism constraints, which is amplified in safety-critical contexts. In this work, we introduce the **C**ausal **C**ompositional **Diff**usion Model (***CCDiff***), a structure-guided diffusion framework to address these challenges. We first formulate the learning of controllable and realistic closed-loop simulation as a constrained optimization problem. Then, CCDiff maximizes controllability while adhering to realism by automatically identifying and injecting causal structures directly into the diffusion process, providing structured guidance to enhance both realism and controllability. Through rigorous evaluations on benchmark datasets and in a closed-loop simulator, CCDiff demonstrates substantial gains over state-of-the-art approaches in generating realistic and user-preferred trajectories. Our results show CCDiff's effectiveness in extracting and leveraging causal structures, showing improved closed-loop performance based on key metrics such as collision rate, off-road rate, FDE, and comfort. For more details, welcome to check our project website: https://sites.google.com/view/ccdiff/.
Change3D: Revisiting Change Detection and Captioning from A Video Modeling Perspective
Authors: Duowang Zhu; Xiaohu Huang; Haiyan Huang; Hao Zhou; Zhenfeng Shao
Venue: CVPR 2025 (main)

Abstract: In this paper, we present Change3D, a framework that reconceptualizes the change detection and captioning tasks through video modeling. Recent methods have achieved remarkable success by regarding each pair of bi-temporal images as separate frames. They employ a shared-weight image encoder to extract spatial features and then use a change extractor to capture differences between the two images. However, image feature encoding, being a task-agnostic process, cannot attend to changed regions effectively. Furthermore, different change extractors designed for various change detection and captioning tasks make it difficult to have a unified framework. To tackle these challenges, Change3D regards the bi-temporal images as comprising two frames akin to a tiny video. By integrating learnable perception frames between the bi-temporal images, a video encoder enables the perception frames to interact with the images directly and perceive their differences. Therefore, we can get rid of the intricate change extractors, providing a unified framework for different change detection and captioning tasks. We verify Change3D on multiple tasks, encompassing change detection (including binary change detection, semantic change detection, and building damage assessment) and change captioning, across eight standard benchmarks. Without bells and whistles, this simple yet effective framework can achieve superior performance with an ultra-light video model comprising only ~6%-13% of the parameters and ~8%-34% of the FLOPs compared to state-of-the-art methods. We hope that Change3D could be an alternative to 2D-based models and facilitate future research.
DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling
Authors: Xin Xie; Dong Gong
Venue: CVPR 2025 (main)

Abstract: Text-to-image diffusion model alignment is critical for improving the alignment between the generated images and human preferences. While training-based methods are constrained by high computational costs and dataset requirements, training-free alignment methods remain underexplored and are often limited by inaccurate guidance. We propose a plug-and-play training-free alignment method, DyMO, for aligning the generated images and human preferences during inference. Apart from text-aware human preference scores, we introduce a semantic alignment objective for enhancing the semantic alignment in the early stages of diffusion, relying on the fact that the attention maps are effective reflections of the semantics in noisy images. We propose dynamic scheduling of multiple objectives and intermediate recurrent steps to reflect the requirements at different steps. Experiments with diverse pre-trained diffusion models and metrics demonstrate the effectiveness and robustness of the proposed method.
HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation
Authors: Yiming Liang; Tianhan Xu; Yuta Kikuchi
Venue: CVPR 2025 (main)

Abstract: We present Hierarchical Motion Representation (HiMoR), a novel deformation representation for 3D Gaussian primitives capable of achieving high-quality monocular dynamic 3D reconstruction. The insight behind HiMoR is that motions in everyday scenes can be decomposed into coarser motions that serve as the foundation for finer details. Using a tree structure, HiMoR's nodes represent different levels of motion detail, with shallower nodes modeling coarse motion for temporal smoothness and deeper nodes capturing finer motion. Additionally, our model uses a few shared motion bases to represent motions of different sets of nodes, aligning with the assumption that motion tends to be smooth and simple. This motion representation design provides Gaussians with a more structured deformation, maximizing the use of temporal relationships to tackle the challenging task of monocular dynamic 3D reconstruction. We also propose using a more reliable perceptual metric as an alternative, given that pixel-level metrics for evaluating monocular dynamic 3D reconstruction can sometimes fail to accurately reflect the true quality of reconstruction. Extensive experiments demonstrate our method's efficacy in achieving superior novel view synthesis from challenging monocular videos with complex motions.
GENIUS: A Generative Framework for Universal Multimodal Search
Authors: Sungyeon Kim; Xinliang Zhu; Xiaofan Lin; Muhammet Bastan; Douglas Gray; Suha Kwak
Venue: CVPR 2025 (main)

Abstract: Generative retrieval is an emerging approach in information retrieval that generates identifiers (IDs) of target data based on a query, providing an efficient alternative to traditional embedding-based retrieval methods. However, existing models are task-specific and fall short of embedding-based retrieval in performance. This paper proposes GENIUS, a universal generative retrieval framework supporting diverse tasks across multiple modalities and domains. At its core, GENIUS introduces modality-decoupled semantic quantization, transforming multimodal data into discrete IDs encoding both modality and semantics. Moreover, to enhance generalization, we propose a query augmentation that interpolates between a query and its target, allowing GENIUS to adapt to varied query forms. Evaluated on the M-BEIR benchmark, it surpasses prior generative methods by a clear margin. Unlike embedding-based retrieval, GENIUS consistently maintains high retrieval speed across database size, with competitive performance across multiple benchmarks. With additional re-ranking, GENIUS often achieves results close to those of embedding-based methods while preserving efficiency.
Enhanced Visual-Semantic Interaction with Tailored Prompts for Pedestrian Attribute Recognition
Authors: Junyi Wu; Yan Huang; Min Gao; Yuzhen Niu; Yuzhong Chen; Qiang Wu
Venue: CVPR 2025 (main)

Abstract: Pedestrian attribute recognition (PAR) seeks to predict multiple semantic attributes associated with a specific pedestrian. There are two types of approaches for PAR: unimodal framework and bimodal framework. The former one is to seek a robust visual feature. However, the lack of exploiting semantic feature of linguistic modality is the main concern. The latter one utilizes prompt learning techniques to integrate linguistic data. However, static prompt templates and simple bimodal concatenation cannot to capture the extensive intra-class attribute variability and support active modalities collaboration. In this paper, we propose an Enhanced Visual-Semantic Interaction with Tailored Prompts (EVSITP) framework for PAR. We present an Image-Conditional Dual-Prompt Initialization Module (IDIM) to adaptively generate context-sensitive prompts from visual inputs. Subsequently, a Prompt Enhanced and Regularization Module (PERM) is proposed to strengthen linguistic information from IDIM. We further design a Bimodal Mutual Interaction Module (BMIM) to ensure bidirectional modalities communication.  In addition, existing PAR datasets are collected over a short period in limited scenarios, which do not align with real-world scenarios. Therefore, we annotate a long-term person re-identification dataset to create a new PAR dataset, Celeb-PAR. Experiments on several challenging PAR datasets show that our method outperforms state-of-the-art approaches.
SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement
Authors: Mark Boss; Zixuan Huang; Aaryaman Vasishta; Varun Jampani
Venue: CVPR 2025 (main)

Abstract: We present SF3D, a novel method for rapid and high-quality textured object mesh reconstruction from a single image in just 0.5 seconds. Unlike most existing approaches, SF3D is explicitly trained for mesh generation, incorporating a fast UV unwrapping technique that enables swift texture generation rather than relying on vertex colors. The method also learns to predict material parameters and normal maps to enhance the visual quality of the reconstructed 3D meshes. Furthermore, SF3D integrates a delighting step to effectively remove low-frequency illumination effects, ensuring that the reconstructed meshes can be easily used in novel illumination conditions. Experiments demonstrate the superior performance of SF3D over the existing techniques
HSI-GPT: A General-Purpose Large Scene-Motion-Language Model for Human Scene Interaction
Authors: Yuan Wang; Yali Li; Xiang Li; Shengjin Wang
Venue: CVPR 2025 (main)

Abstract: 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.