GenAI4ELab/papercli-papers huggingface dataset:
Dynamic-Static Decomposition for Novel View Synthesis of Dynamic Scenes with Spiking Neurons
Authors: Lingyun Dai; Zehao Chen; Yan Liu; Shi Gu; Peng Lin; De Ma; Huajin Tang; Qian Zheng; Gang Pan
Venue: CVPR 2026 (main)

Abstract: Novel view synthesis for dynamic scenes remains challenging due to complex motion variations. Recent methods represent dynamic and static regions with separate Gaussians to improve efficiency and accuracy, but inaccurate assignment of static and dynamic Gaussian primitives still limits performance. We identify two key issues, namely inaccurate mask priors and improper tag representations, which lead to boundary artifacts, loss of fine-grained motion details, and overfitting on input views, resulting in degraded side-view synthesis. To address these problems, we propose a spatio-temporally fine-grained mask field and a discontinuous dynamic-static tagging field to achieve accurate assignment of dynamic and static Gaussian primitives, enabling high-quality novel view synthesis, especially in fine-grained motions, motion boundary regions, and side viewpoints. Experiments show that our method achieves state-of-the-art rendering quality and real-time performance.
Hierarchical Visual Relocalization with Nearest View Synthesis from Feature Gaussian Splatting
Authors: Huaqi Tao; Bingxi Liu; Guangcheng Chen; Fulin Tang; Li He; Hong Zhang
Venue: CVPR 2026 (main)

Abstract: Visual relocalization is a fundamental task in the field of 3D computer vision, estimating a camera's pose when it revisits a previously known scene. While point-based hierarchical relocalization methods have shown strong scalability and efficiency, they are often limited by sparse image observations and weak feature matching. In this work, we propose SplatHLoc, a novel hierarchical visual relocalization framework that uses Feature Gaussian Splatting as the scene representation. To address the sparsity of database images, we propose an adaptive viewpoint retrieval method that synthesizes virtual candidates with viewpoints more closely aligned with the query, thereby improving the accuracy of initial pose estimation. For feature matching, we observe that Gaussian-rendered features and those extracted directly from images exhibit different strengths across the two-stage matching process: the former performs better in the coarse stage, while the latter proves more effective in the fine stage. Therefore, we introduce a hybrid feature matching strategy, enabling more accurate and efficient pose estimation. Extensive experiments on both indoor and outdoor datasets show that SplatHLoc enhances the robustness of visual relocalization, setting a new state-of-the-art.
Unleashing Vision-Language Semantics for Deepfake Video Detection
Authors: Jiawen Zhu; Yunqi Miao; Xueyi Zhang; Jiankang Deng; Guansong Pang
Venue: CVPR 2026 (main)

Abstract: Recent Deepfake Video Detection (DFD) studies have demonstrated that pre-trained Vision-Language Models (VLMs) such as CLIP exhibit strong generalization capabilities in detecting artifacts across different identities. However, existing approaches focus on leveraging visual features only, overlooking their most distinctive strength -- the rich vision-language semantics embedded in the latent space. We propose VLAForge, a novel DFD framework that unleashes the potential of such cross-modal semantics to enhance model's discriminability in deepfake detection. This work i) enhances the visual perception of VLM through a ForgePerceiver, which acts as an independent learner to capture diverse, subtle forgery cues both granularly and holistically, while preserving the pretrained Vision-Language Alignment (VLA) knowledge, and ii) provides a complementary discriminative cue -- Identity-Aware VLA score, derived by coupling cross-modal semantics with the forgery cues learned by ForgePerceiver. Notably, the VLA score is augmented by an identity prior-informed text prompting to capture authenticity cues tailored to each identity, thereby enabling more discriminative cross-modal semantics. Comprehensive experiments on video DFD benchmarks, including classical face-swapping forgeries and recent full-face generation forgeries, demonstrate that our VLAForge substantially outperforms state-of-the-art methods at both frame and video levels. Code is available at https://github.com/mala-lab/VLAForge.
Protect to Adapt: Orthogonal Subspace Control with Ranked Negative-Prompt Curriculum for Few-Shot Action Recognition
Authors: Hantao Qi; Yan Yan; Junlong Gao; Hanzi Wang
Venue: CVPR 2026 (main)

Abstract: Adapting Vision-Language Models (VLMs) to few-shot action recognition (FSAR) often trades accuracy for stability: task-specific gains can trigger catastrophic forgetting of domain-general knowledge and reduce inter-class margins. In few-shot episodes, each query is contrasted with only one positive class and a few negatives, so the text encoder sees limited prompt diversity and rarely observes hard counter-examples near decision boundaries. We propose Protect-to-Adapt (P2A), a parameter-efficient fine-tuning method with two complementary modules. Orthogonal Subspace Control (OSC) estimates a principal semantic subspace of the pre-trained backbone and constrains low-rank updates to its orthogonal complement, preserving domain-general semantics while allowing task-specific adaptation. Ranked Negative-prompt Curriculum (RNC) uses a large language model to generate verifier-filtered negative prompts with increasing difficulty. These class-specific hard counter-examples enlarge margins and sharpen decision boundaries under few-shot conditions. With only 2% of backbone parameters trainable, P2A achieves state-of-the-art performance on five FSAR benchmarks and substantially reduces catastrophic forgetting in a cross-dataset continual-learning setting where the model is adapted sequentially to multiple video datasets without replay.
MaskFocus: Focusing Policy Optimization on Critical Steps for Masked Image Generation
Authors: Guohui Zhang; Hu Yu; Xiaoxiao Ma; Yaning Pan; Hang Xu; Jie Huang; Feng Zhao
Venue: CVPR 2026 (main)

Abstract: Reinforcement learning (RL) has demonstrated significant potential for post-training language models and autoregressive visual generative models, but adapting RL to masked generative models (MGMs) remains challenging. The core factor is that policy optimization requires the probability likelihood of each step due to its multi-step iteration process. This reliance on entire sampling trajectories introduces high computational cost, whereas natively optimizing random steps often yields suboptimal results. In this paper, we present MaskFocus, a novel RL framework that achieves effective policy optimization for MGMs by focusing on critical steps. Specifically, we determine the step-level information gain by measuring the similarity between the intermediate images at each sampling step and the final generated image. Crucially, we leverage this to identify the most critical and valuable steps and execute policy optimization on them. Furthermore, we design a dynamic routing sampling based on entropy to encourage the model to explore more valuable masking strategies. Extensive experiments on multiple Text-to-Image benchmarks validate the effectiveness of our method.
Differentially Private 2D Human Pose Estimation
Authors: Kaushik Bhargav Sivangi; Paul Henderson; Fani Deligianni
Venue: CVPR 2026 (main)

Abstract: Human pose estimation (HPE) underpins critical applications in healthcare, activity recognition, and human-computer interaction. However, the privacy implications of processing sensitive visual data present significant deployment barriers in critical domains. %Conventional anonymization techniques offer weak protection and are unquantifiable, while Differential Privacy (DP) provides formal guarantees but often results in steep performance costs.We introduce the first unified framework for differentially private 2D Human Pose Estimation (2D-HPE) that achieves strong privacy-utility trade-offs for structured visual prediction through complementary noise mitigation mechanisms. Our Feature-Projective DP integrates: (1) subspace projection that reduces noise variance by a factor k/p by restricting gradient updates to a k-principal subspace within the full p-dimensional parameter space, and (2) feature-level privacy, which selectively privatizes sensitive features while retaining public visual cues. Together these mechanisms yield a multiplicative utility gain under formal privacy constraints.%We further propose a feature-projective hybrid that combines both the mechanisms within a single post-processing framework.Extensive experiments on MPII and HumanART datasets across privacy budgets (\varepsilon \in \ 0.2, 0.4, 0.6, 0.8\ ), clipping thresholds (C \in  \ 0.01, 0.1, 1.0\ ) and training strategies demonstrate consistent improvements over vanilla DP-SGD. At \varepsilon=0.8, our method achieves 82.61% PCKh@0.5, recovering 73% of the privacy induced performance gap. Cross-dataset evaluation on the HumanART confirms generalization (51.6 AP). Our study provides the first rigorous benchmark and a practical blueprint for privacy-preserving pose estimation in sensitive, real-world applications. Our source code will be made public on acceptance.
MetricHMSR: Metric Human Mesh and Scene Recovery from Monocular Images
Authors: Chentao Song; He Zhang; Haolei Yuan; Haozhe Lin; Jianhua Tao; Hongwen Zhang; Tao Yu
Venue: CVPR 2026 (main)

Abstract: We introduce MetricHMSR (Metric Human Mesh and Scene Recovery), a novel approach for metric human mesh and scene recovery from monocular images. Due to unrealistic assumptions in the camera model and inherent challenges in metric perception, existing approaches struggle to achieve human pose and metric 3D position estimation through a unified module.To address this limitation, MetricHMSR incorporates camera rays to comprehensively encode both the bounding box information and the intrinsic parameters of perspective projection. Then we proposed Human Mixture-of-Experts (MoE), the model dynamically routes image features and ray features to task-specific experts for specialized understanding of different data aspects, enabling a unified framework that simultaneously perceives the local pose and the global 3D position.Based on the results above, we further refine the existing monocular metric depth estimation method to achieve more accurate results, ultimately enabling the seamless overlay of humans and scenes in 3D space.Comprehensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on both human mesh and scene recovery.
Uni-Hema: Unified Model for Digital Hematopathology
Authors: Abdul Rehman; Iqra Rasool; Ayisha Imran; Mohsen Ali; Waqas Sultani
Venue: CVPR 2026 (main)

Abstract: Digital hematopathology requires cell-level analysis across diverse disease categories, including malignant disorders (e.g., leukemia), infectious conditions (e.g., malaria), and non-malignant red blood cell disorders (e.g., sickle cell disease). Whether single-task, vision-language, WSI- optimized, or single-cell hematology models, these approaches share a key limitation: they cannot provide unified, multi-task, multi-modal reasoning across the complexities of digital hematopathology. To overcome these limitations, we propose Uni-Hema, a multi-task, unified model for digital hematopathology integrating detection, classification, segmentation, morphology prediction, and reasoning across multiple diseases. Uni-Hema leverages 46 publicly available datasets, encompassing over 700K images and 21K question-answer pairs, and is built upon Hema-Former, a multimodal module that bridges visual and linguistic representations at the hierarchy level for the different tasks (detection, classification, segmentation, morphology, mask language modeling, and visual question answering) at different granularities. Extensive experiments demonstrate that Uni-Hema achieves comparable or superior performance compared to training on a single task and single-dataset models, across diverse hematological tasks, while providing interpretable, morphologically relevant insights at the single-cell level. Our framework establishes a new standard for multi-task and multi-modal digital hematopathology. The code is available at https://github.com/intelligentMachines-ITU/Uni-Hema
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Authors: Jovana Kondic; Pengyuan Li; Dhiraj Joshi; Isaac Sanchez; Ben Wiesel; Shafiq Abedin; Amit Alfassy; Eli Schwartz; Daniel Caraballo; Yagmur Gizem Cinar; Florian Scheidegger; Steven I. Ross; Daniel Karl I. Weidele; Hang Hua; Ekaterina Arutyunova; Roei Herzig; Zihan Wang; Xinyue Yu; Yunfei Zhao; Sicong Jiang; Minghao Liu; Qunshu Lin; Aude Oliva; Rogerio Feris
Venue: CVPR 2026 (main)

Abstract: Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a novel code-guided synthesis pipeline to generate 1.5 million diverse chart samples spanning 24 chart types and 6 plotting libraries. Each sample consists of five aligned components: plotting code, rendered chart image, data table, natural language summary, and question-answering with reasoning, providing fine-grained cross-modal alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations.  Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at https://huggingface.co/datasets/ibm-granite/ChartNet
MapRoute:Precise-Concept Erasing Mappers via Semantic Routing
Authors: Sihao Li; Baixi Liang; Shuohong Xia; Yunyun Yang
Venue: CVPR 2026 (main)

Abstract: Contemporary commercial and open-source diffusion models have demonstrated remarkable performance in text-to-image generation, enabling widespread applications in creative design and content creation. However, legitimate requirements--such as copyright protection, privacy compliance, or personalized customization--often necessitate the removal of specific semantic concepts from pretrained models. Existing concept erasure methods suffer from two critical limitations: (1) **Incomplete suppression**, where the model still occasionally generates images containing the target concept; (2) **Poor semantic selectivity**, which degrades the generation quality of unrelated concepts and compromises overall model utility.To address these challenges, we propose **`MapRoute`**, a lightweight, semantics-aware concept erasure framework based on dynamic routing. Our approach introduces a set of modular components--termed *Mappers*--placed after a frozen pretrained text encoder. Each Mapper learns a linear mapping from a target concept to a surrogate concept. During inference, the system dynamically activates the top-K Mappers most relevant to the input prompt, based on cosine similarity between the text embedding and all the target concept embeddings, and applies their transformations sequentially. This input-driven, modular intervention enables precise, on-demand erasure while avoiding unnecessary interference with irrelevant semantics.Extensive experiments demonstrate that **`MapRoute`** effectively suppresses specified concepts while significantly reducing collateral damage to unrelated concept. By operating without full-model fine-tuning, our method entirely avoids parameter drift and concept erosion. Moreover, **`MapRoute`** outperforms state-of-the-art baselines in terms of generation fidelity, semantic consistency, and scalability to multi-concept erasure scenarios.
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
Authors: Beining Han; Yu-Wei Chao; Erwin Coumans; Clemens Eppner; Jia Deng; Stan Birchfield; Adithyavairavan Murali
Venue: CVPR 2026 (main)

Abstract: We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and physical grasping processes. Our method extends diffusion model based generative 6-DOF grasping models to condition on the additional gripper's representation. We propose a swept-volume heuristic for encoding the gripper. We train our cross-embodiment model with procedural grippers and a large-scale dataset of 395 Million grasps. In simulation experiments, our model has the best zero-shot generalization to novel real-world grippers and objects over baseline methods. Our model also serves as a good initialization for fine-tuning to adapt to novel grippers. In ablations, we demonstrate the efficiency of our sweep-volume gripper representation and our procedural gripper training dataset. Last, we show zero-shot generalization to real-world novel grippers for 6-DOF grasping, surpassing baselines in cross-embodiment generalization.
Not All Birds Look The Same: Identity-Preserving Generation For Birds
Authors: Aaron Sun; Oindrila Saha; Subhransu Maji
Venue: CVPR 2026 (main)

Abstract: Since the advent of controllable image generation, increasingly rich modes of control have enabled greater customization and accessibility for everyday users.Zero-shot, identity-preserving models such as Insert Anything and OminiControl now support applications like virtual try-on without requiring additional fine-tuning.While these models may be fitting for humans and rigid everyday objects, they still have limitations for non-rigid or fine-grained categories. These domains often lack accessible, high-quality data--especially videos or multi-view observations of the same subject--making them difficult both to evaluate and to improve upon. Yet, such domains are essential for moving beyond content creation toward applications that demand accuracy and fine detail.Birds are an excellent domain for this task: they exhibit high diversity, require fine-grained cues for identification, and come in a wide variety of poses. We introduce the NABirds Look-Alikes (NABLA) dataset, consisting of 4,759 expert-curated image pairs. Together with 1,073 pairs collected from multi-image observations on iNaturalist and a small set of videos, this forms a benchmark for evaluating identity-preserving generation of birds.We show that state-of-the-art baselines fail to maintain identity on this dataset, and we demonstrate that training on images grouped by species, age, and sex--used as a proxy for identity--substantially improves performance on both seen and unseen species.
Geometric-Aware Hypergraph Reasoning for Novel Class Discovery in Point Cloud Segmentation
Authors: Zihao Zhang; Aming Wu; Yang Li; Yahong Han; Jialie Shen
Venue: CVPR 2026 (main)

Abstract: Novel Class Discovery in Point Cloud Segmentation is recently proposed, aiming to leverage knowledge from known classes to automatically segment unlabeled classes within point clouds. The core of this task lies in leveraging the geometric and semantic knowledge of multiple known classes to achieve semantic understanding and segmentation of novel classes.However, existing methods overlook the high-order associations between known and novel classes, relying solely on binary associations for class assignment and novel class reasoning, which leads to less precise semantic segmentation.To address these issues, we introduce a hypergraph structure to model high-order associations among classes, enabling collaborative reasoning from known classes to novel classes, extending beyond traditional binary relations.Additionally, existing methods focus excessively on extracting semantic information when processing point cloud data, neglecting the importance of geometric features. To address this, we introduce Geometric-Aware Prototypes, enhancing the model's ability to capture geometric spatial information.By propagating geometric information through hyperedges, our method enhances the understanding of spatial distributions across classes, improving segmentation accuracy.Significant performance improvements achieved on the SemanticKITTI and SemanticPOSS datasets demonstrate the superiority of our method.
Discriminative Perception via Anchored Description for Reasoning Segmentation
Authors: Tao Yang; Qing Zhou; Yanliang Li; Qi Wang
Venue: CVPR 2026 (main)

Abstract: Reasoning segmentation increasingly employs reinforcement learning to generate explanatory reasoning chains that guide Multimodal Large Language Models. While these geometric rewards are primarily confined to guiding the final localization, they are incapable of discriminating whether the reasoning process remains anchored on the referred region or strays into irrelevant context. Lacking this discriminative guidance, the model's reasoning often devolves into unfocused and verbose chains that ultimately fail to disambiguate and perceive the target in complex scenes. This suggests a need to complement the RL objective with Discriminative Perception, an ability to actively distinguish a target from its context. To realize this, we propose DPAD to compel the model to generate a descriptive caption of the referred object, which is then used to explicitly discriminate by contrasting the caption's semantic relevance to the referred object against the wider context. By optimizing for this discriminative capability, the model is forced to focus on the unique attributes of the target, leading to a more converged and efficient reasoning chain. The descriptive caption also serves as an interpretability rationale that aligns with the segmentation. Experiments on the benchmarks confirm the validity of our approach, delivering substantial performance gains, with the cIoU on ReasonSeg increasing by 3.09% and the reasoning chain length decreasing by approximately 42%. Code is available at https://github.com/mrazhou/DPAD.
Dual Band Thermal Videography: Separating Time-Varying Reflection and Emission Near Ambient Conditions
Authors: Sriram Narayanan; Mani Ramanagopal; Srinivasa Narasimhan
Venue: CVPR 2026 (main)

Abstract: Long-wave infrared radiation captured by a thermal camera includes (a) emission from an object governed by its temperature and emissivity, and (b) reflected radiation from the surrounding environment. Separating these components is a long-standing challenge in thermography. Even when using multiple bands, the problem is under-determined without priors on emissivity. This difficulty is amplified in near ambient conditions, where emitted and reflected signals are of comparable magnitude. We present a dual-band video thermography framework that reduces this ambiguity by combining two complementary ideas at a per-pixel level: (i) spectral cues (ratio of emissivity between bands is unknown but fixed), and (ii) temporal cues (object radiation changes smoothly while background radiation changes rapidly). We derive an image formation model and an algorithm to jointly estimate the object's emissivity at each band, and the time-varying object and background temperatures. Experiments with calibrated and uncalibrated emissivities in everyday scenes (e.g., coffee pot heating up, palm print on mirrors, reflections of moving people), demonstrate robust separation and recovery of temperature fields.