GenAI4ELab/papercli-papers huggingface dataset:
Relation3D : Enhancing Relation Modeling for Point Cloud Instance Segmentation
Authors: Jiahao Lu; Jiacheng Deng
Venue: CVPR 2025 (main)

Abstract: 3D instance segmentation aims to predict a set of object instances in a scene, representing them as binary foreground masks with corresponding semantic labels. Currently, transformer-based methods are gaining increasing attention due to their elegant pipelines and superior predictions. However, these methods primarily focus on modeling the external relationships between scene features and query features through mask attention. They lack effective modeling of the internal relationships among scene features as well as between query features.In light of these disadvantages, we propose Relation3D: Enhancing Relation Modeling for Point Cloud Instance Segmentation. Specifically, we introduce an adaptive superpoint aggregation module and a contrastive learning-guided superpoint refinement module to better represent superpoint features (scene features) and leverage contrastive learning to guide the updates of these features.Furthermore, our relation-aware self-attention mechanism enhances the capabilities of modeling relationships between queries by incorporating positional and geometric relationships into the self-attention mechanism.Extensive experiments on the ScanNetV2, ScanNet++, ScanNet200 and S3DIS datasets demonstrate the superior performance of Relation3D.
Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions
Authors: Ting-Hsuan Liao; Yi Zhou; Yu Shen; Chun-Hao Paul Huang; Saayan Mitra; Jia-Bin Huang; Uttaran Bhattacharya
Venue: CVPR 2025 (main)

Abstract: We explore how body shapes influence human motion synthesis, an aspect often overlooked in existing text-to-motion generation methods due to the ease of learning a homogenized, canonical body shape. However, this homogenization can distort the natural correlations between different body shapes and their motion dynamics. Our method addresses this gap by generating body-shape-aware human motions from natural language prompts. We utilize a finite scalar quantization-based variational autoencoder (FSQ-VAE) to quantize motion into discrete tokens and then leverage continuous body shape information to de-quantize these tokens back into continuous, detailed motion. Additionally, we harness the capabilities of a pretrained language model to predict both continuous shape parameters and motion tokens, facilitating the synthesis of text-aligned motions and decoding them into shape-aware motions. We evaluate our method quantitatively and qualitatively, and also conduct a comprehensive perceptual study to demonstrate its efficacy in generating shape-aware motions.
Generating 3D-Consistent Videos from Unposed Internet Photos
Authors: Gene Chou; Kai Zhang; Sai Bi; Hao Tan; Zexiang Xu; Fujun Luan; Bharath Hariharan; Noah Snavely
Venue: CVPR 2025 (main)

Abstract: We address the problem of generating videos from unposed internet photos. A handful of input images serve as keyframes, and our model interpolates between them to simulate a path moving between the cameras. Given random images, a model's ability to capture underlying geometry, recognize scene identity, and relate frames in terms of camera position and orientation reflects a fundamental understanding of 3D structure and scene layout. However, existing video models such as Luma Dream Machine fail at this task. We design a self-supervised method that takes advantage of the consistency of videos and variability of multiview internet photos to train a scalable, 3D-aware video model without any 3D annotations such as camera parameters. We validate that our method outperforms commercial models in terms of geometric and appearance consistency. We also show our model benefits applications that enable camera control, such as 3D Gaussian Splatting. Our results suggest that we can scale up scene-level 3D learning using only 2D data such as videos and multiview internet photos.
Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging
Authors: Bo Wang; Dingwei Tan; Yen-Ling Kuo; Zhaowei Sun; Jeremy M. Wolfe; Tat-Jen Cham; Mengmi Zhang
Venue: CVPR 2025 (main)

Abstract: Imagine searching a collection of coins for quarters (0.25), dimes (0.10), nickels (0.05), and pennies (0.01)--a hybrid foraging task where observers search for multiple instances of multiple target types. In such tasks, how do target values and their prevalence influence foraging and eye movement behaviors (e.g., should you prioritize rare quarters or common nickels)? To explore this, we conducted human psychophysics experiments, revealing that humans are proficient reward foragers. Their eye fixations are drawn to regions with higher average rewards, fixation durations are longer on more valuable targets, and their cumulative rewards exceed chance, approaching the upper bound of optimal foragers. To probe these decision making process of humans, we developed a transformer-based Visual Forager (VF) model trained via reinforcement learning. Our VF model takes a series of targets, their corresponding values, and the search image as inputs, processes the images using foveated vision, and produces a sequence of eye movements along with decisions on whether to click on each fixated item. Our model outperforms all baselines, achieves cumulative rewards comparable to those of humans, and closely mirrors human foraging behavior in eye movements and click biases. Furthermore, stress tests on out-of-distribution tasks with novel targets, unseen values, and varying set sizes demonstrate the VF model's effective generalization. Our work offers valuable insights into the relationship between eye movements and decision-making, with our model serving as a powerful tool for further exploration of this connection. All data, code, and models are available at https://github.com/ZhangLab-DeepNeuroCogLab/visual-forager.
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
Authors: Zhengrui Guo; Conghao Xiong; Jiabo Ma; Qichen Sun; Lishuang Feng; Jinzhuo Wang; Hao Chen
Venue: CVPR 2025 (main)

Abstract: Few-shot learning presents a critical solution for cancer diagnosis in computational pathology (CPath), addressing fundamental limitations in data availability, particularly the scarcity of expert annotations and patient privacy constraints. A key challenge in this paradigm stems from the inherent disparity between the limited training set of whole slide images (WSIs) and the enormous number of contained patches, where a significant portion of these patches lacks diagnostically relevant information, potentially diluting the model's ability to learn and focus on critical diagnostic features. While recent works attempt to address this by incorporating additional knowledge, several crucial gaps hinder further progress: (1) despite the emergence of powerful pathology foundation models (FMs), their potential remains largely untapped, with most approaches limiting their use to basic feature extraction; (2) current language guidance mechanisms attempt to align text prompts with vast numbers of WSI patches all at once, struggling to leverage rich pathological semantic information. To this end, we introduce the knowledge-enhanced adaptive visual compression framework, dubbed FOCUS, which uniquely combines pathology FMs with language prior knowledge to enable a focused analysis of diagnostically relevant regions by prioritizing discriminative WSI patches. Our approach implements a progressive three-stage compression strategy: we first leverage FMs for global visual redundancy elimination, and integrate compressed features with language prompts for semantic relevance assessment, then perform neighbor-aware visual token filtering while preserving spatial coherence. Extensive experiments on pathological datasets spanning breast, lung, and ovarian cancers demonstrate its superior performance in few-shot pathology diagnosis. Codes are available at https://github.com/dddavid4real/FOCUS.
Beyond Human Perception: Understanding Multi-Object World from Monocular View
Authors: Keyu Guo; Yongle Huang; Shijie Sun; Xiangyu Song; Mingtao Feng; Zedong Liu; Huansheng Song; Tiantian Wang; Jianxin Li; Naveed Akhtar; Ajmal Saeed Mian
Venue: CVPR 2025 (main)

Abstract: Language and binocular vision play a crucial role in human understanding of the world. Advancements in artificial intelligence have also made it possible for machines to develop 3D perception capabilities essential for high-level scene understanding. However, only monocular cameras are often available in practice due to cost and space constraints. Enabling machines to achieve accurate 3D understanding from a monocular view is practical but presents significant challenges. We introduce MonoMulti-3DVG, a novel task aimed at achieving multi-object 3D Visual Grounding (3DVG) based on monocular RGB images, allowing machines to better understand and interact with the 3D world. To this end, we construct a large-scale benchmark dataset, MonoMulti3D-ROPE, and propose a model, CyclopsNet that integrates a State-Prompt Visual Encoder (SPVE) module with a Denoising Alignment Fusion (DAF) module to achieve robust multi-modal semantic alignment and fusion. This leads to more stable and robust multi-modal joint representations for downstream tasks. Experimental results show that our method significantly outperforms existing techniques on the MonoMulti3D-ROPE dataset. Our dataset and code are available at https://github.com/JasonHuang516/MonoMulti-3DVG
GRAE-3DMOT: Geometry Relation-Aware Encoder for Online 3D Multi-Object Tracking
Authors: Hyunseop Kim; Hyo-Jun Lee; Yonguk Lee; Jinu Lee; Hanul Kim; Yeong Jun Koh
Venue: CVPR 2025 (main)

Abstract: Recently, 3D multi-object tracking (MOT) has widely adopted the standard tracking-by-detection paradigm, which solves the association problem between detections and tracks. Many tracking-by-detection approaches establish constrained relationships between detections and tracks using a distance threshold to reduce confusion during association. However, this approach does not effectively and comprehensively utilize the information regarding objects due to the constraints of the distance threshold. In this paper, we propose GRAE-3DMOT, Geometry Relation-Aware Encoder 3D Multi-Object Tracking, which contains a geometric relation-aware encoder to produce informative features for association. The geometric relation-aware encoder consists of three components: a spatial relation-aware encoder, a spatiotemporal relation-aware encoder, and a distance-aware feature fusion layer. The spatial relation-aware encoder effectively aggregates detection features by comprehensively exploiting as many detections as possible. The spatiotemporal relation-aware encoder provides spatiotemporal relation-aware features by combing spatial and temporal relation features, where the spatiotemporal relation-aware features are transformed into association scores for MOT. The distance-aware feature fusion layer is integrated into both encoders to enhance the relation features of physically proximate objects. Experimental results demonstrate that the proposed GRAE-3DMOT outperforms the state-of-the-art on the nuScenes. Our approach achieves 73.7% and 70.2% AMOTA on the nuScenes validation and test sets using CenterPoint detections.
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression
Authors: Xiaoyi Qu; David Aponte; Colby Banbury; Daniel P. Robinson; Tianyu Ding; Kazuhito Koishida; Ilya Zharkov; Tianyi Chen
Venue: CVPR 2025 (main)

Abstract: Structured pruning and quantization are fundamental techniques used to reduce the size of deep neural networks (DNNs) and typically are applied independently. Applying these techniques jointly via co-optimization has the potential to produce smaller, high-quality models. However, existing joint schemes are not widely used because of (1) engineering difficulties (complicated multi-stage processes), (2) black-box optimization (extensive hyperparameter tuning to control the overall compression), and (3) insufficient architecture generalization. To address these limitations, we present the framework GETA, which automatically and efficiently performs joint structured pruning and quantization-aware training on any DNN. GETA introduces three key innovations: (I) a quantization-aware dependency graph (QADG) that constructs a pruning search space for generic quantization-aware DNN, (II) a partially projected stochastic gradient method that guarantees layer-wise bit constraints are satisfied, and (III) a new joint learning strategy that incorporates interpretable relationships between pruning and quantization. We present numerical experiments on both convolutional neural networks and transformer architectures that show that our approach achieves competitive (often superior) performance compared to existing joint pruning and quantization methods. The source code is available at https://github.com/microsoft/GETA.
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
Authors: Seokil Ham; Hee-Seon Kim; Sangmin Woo; Changick Kim
Venue: CVPR 2025 (main)

Abstract: Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain largely unexplored. In our study, we introduce two key insights-driven strategies for PEFT in Mamba architecture: (1) While state-space models (SSMs) have been regarded as the cornerstone of Mamba architecture, then expected to play a primary role in transfer learning, our findings reveal that Projectors---not SSMs---are the predominant contributors to transfer learning, and (2) Based on our observation, we propose a novel PEFT method specialized to Mamba architecture: Projector-targeted Diagonal-centric Linear Transformation (ProDiaL). ProDiaL focuses on optimizing only diagonal-centric linear transformation matrices, without directly fine-tuning the pretrained Projector weights. This targeted approach allows efficient task adaptation, utilizing less than 1% of the total parameters, and exhibits strong performance across both vision and language Mamba models, highlighting its versatility and effectiveness.
ViUniT: Visual Unit Tests for More Robust Visual Programming
Authors: Artemis Panagopoulou; Honglu Zhou; Silvio Savarese; Caiming Xiong; Chris Callison-Burch; Mark Yatskar; Juan Carlos Niebles
Venue: CVPR 2025 (main)

Abstract: Programming based approaches to reasoning tasks have substantially expanded the types of questions models can answer about visual scenes.Yet on benchmark visual reasoning data, when answering correctly, such models produce incorrect programs 33% of the time. These models are often right for the wrong reasons and risk unexpected failures on new data. Unit tests play a foundational role in ensuring code correctness and could be used to repair such failures. We propose Visual Unit Testing (ViUniT), a framework to improve the reliability of visual programs by automatically generating unit tests. In our framework, a unit test is represented as a novel image and answer meant to verify logical correctness of a program produced for a given query. Our method leverages a language model to create unit tests in the form of image descriptions and expected answers and image synthesis to produce corresponding images. We conduct a comprehensive analysis of what constitutes an effective visual unit test suite, exploring unit test generation, sampling strategies, image generation methods, and varying the number of programs and unit tests.  Additionally, we introduce four applications of visual unit tests: best program selection, answer refusal, re-prompting, and unsupervised reward formulations for reinforcement learning.  Experiments with two models across three datasets in visual question answering and image-text matching demonstrate that ViUniT improves model performance by 11.4%. Notably, it enables 7B open-source models to outperform gpt-4o-mini by an average of 7.7% and reduces the occurrence of programs that are correct for the wrong reasons by 40%.
LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields
Authors: Zhengqin Li; Dilin Wang; Ka Chen; Zhaoyang Lv; Thu Nguyen-Phuoc; Milim Lee; Jia-Bin Huang; Lei Xiao; Yufeng Zhu; Carl S. Marshall; Yuheng Ren; Richard Newcombe; Zhao Dong
Venue: CVPR 2025 (main)

Abstract: We present Large Inverse Rendering Model (LIRM), a transformer architecture that jointly reconstructs high-quality shape, materials, and radiance fields with view-dependent effects in less than a second. Our model builds upon the recent Large Reconstruction Models (LRMs) that achieve state-of-the-art sparse-view reconstruction quality. However, existing LRMs struggle to reconstruct unseen parts accurately and cannot recover glossy appearance or generate relightable 3D contents that can be consumed by standard Graphics engines. To address these limitations, we make three key technical contributions to build a more practical multi-view 3D reconstruction framework. First, we introduce an update model that allows us to progressively add more input views to improve our reconstruction. Second, we propose a hexa-plane neural SDF representation to better recover detailed textures, geometry and material parameters. Third, we develop a novel neural directional-embedding mechanism to handle view-dependent effects. Trained on a large-scale shape and material dataset with a tailored coarse-to-fine training scheme, our model achieves compelling results. It compared favorably to optimization-based dense-view inverse rendering methods in terms of geometry and relighting accuracy, while requiring only a fraction of the inference time.
DualTalk: Dual-Speaker Interaction for 3D Talking Head Conversations
Authors: Ziqiao Peng; Yanbo Fan; Haoyu Wu; Xuan Wang; Hongyan Liu; Jun He; Zhaoxin Fan
Venue: CVPR 2025 (main)

Abstract: In face-to-face conversations, individuals need to switch between speaking and listening roles seamlessly. Existing 3D talking head generation models focus solely on speaking or listening, neglecting the natural dynamics of interactive conversation, which leads to unnatural interactions and awkward transitions. To address this issue, we propose a new task--multi-round dual-speaker interaction for 3D talking head generation--which requires models to handle and generate both speaking and listening behaviors in continuous conversation. To solve this task, we introduce DualTalk, a novel unified framework that integrates the dynamic behaviors of speakers and listeners to simulate realistic and coherent dialogue interactions. This framework not only synthesizes lifelike talking heads when speaking but also generates continuous and vivid non-verbal feedback when listening, effectively capturing the interplay between the roles. We also create a new dataset featuring 50 hours of multi-round conversations with over 1,000 characters, where participants continuously switch between speaking and listening roles. Extensive experiments demonstrate that our method significantly enhances the naturalness and expressiveness of 3D talking heads in dual-speaker conversations. We recommend watching the supplementary video: https://ziqiaopeng.github.io/dualtalk
MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D Generation
Authors: Jinnan Chen; Lingting Zhu; Zeyu Hu; Shengju Qian; Yugang Chen; Xin Wang; Gim Hee Lee
Venue: CVPR 2025 (main)

Abstract: Recent advances in auto-regressive transformers have revolutionized generative modeling across different domains, from language processing to visual generation, demonstrating remarkable capabilities. However, applying these advances to 3D generation presents three key challenges: the unordered nature of 3D data conflicts with sequential next-token prediction paradigm, conventional vector quantization approaches incur substantial compression loss when applied to 3D meshes, and the lack of efficient scaling strategies for higher resolution latent prediction. To address these challenges, we introduce MAR-3D, which integrates a pyramid variational autoencoder with a cascaded masked auto-regressive transformer (Cascaded MAR) for progressive latent upscaling in the continuous space. Our architecture employs random masking during training and auto-regressive denoising in random order during inference, naturally accommodating the unordered property of 3D latent tokens. Additionally, we propose a cascaded training strategy with condition augmentation that enables efficiently up-scale the latent token resolution with fast convergence. Extensive experiments demonstrate that MAR-3D not only achieves superior performance and generalization capabilities compared to existing methods but also exhibits enhanced scaling capabilities compared to joint distribution modeling approaches (e.g., diffusion transformers).
beta-FFT: Nonlinear Interpolation and Differentiated Training Strategies for Semi-Supervised Medical Image Segmentation
Authors: Ming Hu; Jianfu Yin; Zhuangzhuang Ma; Jianheng Ma; Feiyu Zhu; Bingbing Wu; Ya Wen; Meng Wu; Cong Hu; Bingliang Hu; Quan Wang
Venue: CVPR 2025 (main)

Abstract: Co-training has achieved significant success in the field of semi-supervised learning; however, the *homogenization phenomenon*, which arises from multiple models tending towards similar decision boundaries, remains inadequately addressed. To tackle this issue, we propose a novel algorithm called **&beta;-FFT** from the perspectives of data diversity and training structure.First, from the perspective of data diversity, we introduce a nonlinear interpolation method based on the **Fast Fourier Transform (FFT)**. This method generates more diverse training samples by swapping low-frequency components between pairs of images, thereby enhancing the model's generalization capability. Second, from the structural perspective, we propose a differentiated training strategy to alleviate the homogenization issue in co-training. In this strategy, we apply additional training with labeled data to one model in the co-training framework, while employing linear interpolation based on the **Beta (&beta;)** distribution for the unlabeled data as a regularization term for the additional training. This approach enables us to effectively utilize the limited labeled data while simultaneously improving the model's performance on unlabeled data, ultimately enhancing the overall performance of the system.Experimental results demonstrate that **&beta;-FFT** outperforms current state-of-the-art (SOTA) methods on three public medical image datasets.
Dynamic Group Normalization: Spatio-Temporal Adaptation to Evolving Data Statistics
Authors: Yair Smadar; Assaf Hoogi
Venue: CVPR 2025 (main)

Abstract: Deep neural networks remain vulnerable to statistical variations in data, even with advances in normalization techniques. Existing methods use fixed-size normalization sets, restricting their adaptability to evolving and diverse data characteristics. We introduce Dynamic Group Normalization (DGN), a framework that dynamically adjusts channel grouping based on statistical awareness, ensuring a flexible and optimized formation of channel groups. By leveraging an efficient spatio-temporal mechanism, DGN continuously evaluates inter-channel relationships within layers and across training epochs, ensuring robust and responsive adaptation to the updated data statistics. Extensive evaluations of 34 architectures and 11 computer vision benchmarks show the consistent superiority of DGN over traditional normalization methods. It achieves significant accuracy gains in classification, detection, and segmentation tasks while maintaining computational efficiency. Moreover, it outperforms traditional methods in challenging scenarios, including out-of-distribution generalization, imbalanced long-tailed distributions, and corrupted data.