GenAI4ELab/papercli-papers huggingface dataset:
Mesh4D: 4D Mesh Reconstruction and Tracking from Monocular Video
Authors: Zeren Jiang; Chuanxia Zheng; Iro Laina; Diane Larlus; Andrea Vedaldi
Venue: CVPR 2026 (main)

Abstract: We propose Mesh4D, a feed-forward model for monocular 4D mesh reconstruction. Given a monocular video of a dynamic object, our model reconstructs the object's complete 3D shape and motion, represented as a deformation field. Our key contribution is a compact latent space that encodes the entire animation sequence. This latent space is learned by an autoencoder that, during training, is guided by the skeletal structure of the training objects, providing strong priors on plausible deformations. Crucially, skeletal information is not required at inference time. The encoder employs spatio-temporal attention, yielding a more stable representation of the object's overall deformation. Building on this representation, we train a latent diffusion model that, conditioned on the input video and the mesh reconstructed from the first frame, predicts the full animation in one shot. We evaluate Mesh4D on reconstruction and novel view synthesis benchmarks, outperforming prior methods in recovering accurate 3D shape and deformation.
Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines
Authors: Yusen Cai; Qing Lin; Bhargava Satya Nunna; Mengmi Zhang
Venue: CVPR 2026 (main)

Abstract: Newborns perceive the world with low-acuity, color-degraded, and temporally continuous vision, which gradually sharpens as infants develop. To explore the ecological advantages of such staged "visual diets", we train self-supervised learning (SSL) models on object-centric videos under constraints that simulate infant vision: grayscale-to-color (C), blur-to-sharp (A), and preserved temporal continuity (T)--collectively termed CATDiet. For evaluation, we establish a comprehensive benchmark across ten datasets, covering clean and corrupted image recognition, texture-shape cue conflict tests, silhouette recognition, depth-order classification, and the visual cliff paradigm.All CATDiet variants demonstrate enhanced robustness in object recognition, despite being trained solely on object-centric videos. Remarkably, models also exhibit biologically aligned developmental patterns, including neural plasticity changes mirroring synaptic density in macaque V1 and behaviors resembling infants' visual cliff responses. Building on these insights, CombDiet initializes SSL with CATDiet before standard training while preserving temporal continuity. Trained on object-centric or head-mounted infant videos, CombDiet outperforms standard SSL on both in-domain and out-of-domain object recognition and depth perception. Together, these results suggest that the developmental progression of early infant visual experience offers a powerful reverse-engineering framework for understanding the emergence of robust visual intelligence in machines. All code, data, and models are available at Github.
Conflict-Aware Adaptive Cross-Reconstruction for Multimodal Sentiment Analysis
Authors: Yan Wang; Fuyuan Cao; Xingwang Zhao
Venue: CVPR 2026 (main)

Abstract: Disentanglement-based methods for learning shared representations are widely used in multimodal sentiment analysis. However, existing methods often overlook potential emotional conflicts across modalities within the same sample. They typically adopt intra-modal reconstruction and rely on similarity losses to align shared representations, which may distort the shared semantics under such conflicts. To address these, we propose a Conflict-aware Adaptive Cross-Reconstruction approach (CACR). First, we formally define emotional conflict and design a conflict-aware weighting strategy. This strategy calculates conflict scores for each modality and maps them to the corresponding cross-reconstruction weights. Second, we construct a cross-reconstruction module. It reconstructs each target modality using its own specific features and the shared features of other modalities, achieving implicit alignment of shared representations. Coupled with the aforementioned weights, this module effectively suppresses conflicting modalities and mitigates semantic ambiguity. Furthermore, we develop a fine-grained sentiment refinement module that captures fine-grained cues from visual- and audio-specific features to supplement textual semantics. Extensive experiments on three datasets show that CACR outperforms existing state-of-the-art methods, demonstrating its effectiveness in handling emotional conflict.
Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion
Authors: Bo Li; Yunkuo Lei; Tingting Bao; Hang Yan; Yaxian Wang; Weiping Fu; Lingling Zhang; Jun Liu
Venue: CVPR 2026 (main)

Abstract: Multi-focus image fusion (MFIF) is a crucial technique in image processing, with a key challenge being the generation of decision maps with precise boundaries. However, traditional methods based on heuristic rules and deep learning methods with black-box networks are difficult to generate high-quality decision maps. To overcome this challenge, we introduce neurodynamics-driven coupled neural P (CNP) systems, which are biological neural computation models inspired by spiking mechanisms, to enhance the accuracy of decision maps. Specifically, we first conduct an in-depth analysis of the model's neurodynamics to identify the constraints between the network parameters and the input signals. This solid analysis avoids abnormal continuous firing of neurons and ensures the model accurately distinguishes between focused and unfocused regions, generating high-quality decision maps for MFIF. Based on this analysis, we propose a Neurodynamics-Driven CNP Fusion model (ND-CNPFuse) tailored for the challenging MFIF task. Unlike current ideas of decision map generation, ND-CNPFuse distinguishes between focused and unfocused regions by mapping the source image into interpretable spike matrices. By comparing the number of spikes, an accurate decision map can be generated directly without any post-processing. Extensive experimental results show that ND-CNPFuse achieves new state-of-the-art performance on four classical MFIF datasets, including Lytro, MFFW, MFI-WHU, and Real-MFF. The code is available at https://github.com/MorvanLi/ND-CNPFuse.
MV-RoMa: From Pairwise Matching into Multi-View Track Reconstruction
Authors: Jongmin Lee; Seungyeop Kang; Sungjoo Yoo
Venue: CVPR 2026 (main)

Abstract: Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner, often producing fragmented and geometrically inconsistent tracks when their predictions are chained across views. We propose MV-RoMa, a multi-view dense matching model that jointly estimates dense correspondences from a source image to multiple co-visible targets. Specifically, we design an efficient model architecture which avoids high computational cost of full cross-attention for multi-view feature interaction: (i) multi-view encoder that leverages pair-wise matching results as a geometric prior, and (ii) multi-view matching refiner that refines correspondences using pixel-wise attention. Additionally, we propose a post-processing strategy that integrates our model's consistent multi-view correspondences as high-quality tracks for SfM. Across diverse and challenging benchmarks, MV-RoMa produces more reliable correspondences and substantially denser, more accurate 3D reconstructions than existing sparse and dense matching methods.
EchoPOSE: 6D Pose Estimation of Sparse Echocardiograms for Left-Ventricular 3D Shape Reconstruction
Authors: Lucas Iijima; Yihao Luo; Dario Sesia; Amit Kaura; Jamil Mayet; Choon Hwai Yap
Venue: CVPR 2026 (main)

Abstract: 3D echocardiography provides superior cardiac quantification to traditional 2D echocardiography, which suffers from geometric idealizations and imaging plane misalignment. However, despite its advantages, clinical adoption of 3D echo remains limited due to logistical and visualization challenges. We propose a novel framework that reconstructs the 3D shape of the left ventricle (LV) throughout the cardiac cycle from sparse 2D echocardiographic views routinely acquired in clinical practice, without the need for external hardware or manual tracking. Our method integrates EchoPOSE, a new deep network that automatically estimates the 6D pose (position and orientation) of LV segmentations, with a graph-harmonic algorithm for 3D shape reconstruction. EchoPOSE employs a transformer-based architecture that combines local image features with global multi-view context, and introduces a geometry-aware loss to ensure spatial consistency across intersecting imaging planes. Trained and evaluated on large-scale synthetic data derived from 3D echocardiography and validated on prospectively acquired clinical echocardiograms, EchoPOSE achieves 3.78 mm and 8.65^ \circ  pose errors, yielding 87.5% Dice reconstruction accuracy, 1.44% ejection fraction (EF) error, and 3.03% volume error, surpassing alternative deep learning techniques and classical clinical approaches. Notably, the framework remains robust under suboptimal imaging alignment, suggesting that EchoPOSE can reduce the sonography skills required for transducer positioning and allow minimally trained clinicians to perform echo scans.
FedMOP: Achieving Enhanced Privacy and Performance in Federated Learning via Momentum Orthogonal Projection
Authors: Yunlong Zhao; Xiaoheng Deng; Hongyan Xu; Zhuohua Qiu; Xiaowen Hu; Shan You; Yi Chen; Chang Xu; Xiu Su
Venue: CVPR 2026 (main)

Abstract: Federated Learning (FL) faces a fundamental dilemma: existing defenses against gradient leakage attacks (GLAs) invariably sacrifice model performance for privacy protection through noise injection or gradient clip. We introduce Federated Learning with Momentum-Based Orthogonal Projection (FedMOP), a method that simultaneously achieves strong privacy guarantees and superior model performance. The key insight is to leverage initialization-based offset mechanisms that operate on orthogonal dimensions. For performance enhancement, FedMOP employs gradient orthogonal projection to counteract local drift, effectively offsetting each client's round-training initial model using global statistical context. For privacy protection, it introduces momentum-based trajectory offset hiding, which makes the offset vector inherently unrecoverable by constructing information barriers through private initialization and randomized evolution. These two mechanisms are synergistic rather than antagonistic. Theoretically, we prove convergence preservation and characterize the computationally infeasible inverse problem faced by attackers. Extensive experiments on CIFAR-10/100 and Tiny-ImageNet demonstrate that FedMOP not only defends effectively against state-of-the-art GLAs but also surpasses existing FL methods in both accuracy and convergence speed, validating its ability to jointly enhance privacy and performance in FL.  Codes are available at https://github.com/zyl123456aB/FedMOP.
FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision
Authors: Tobias Kirschstein; Simon Giebenhain; Matthias Nießner
Venue: CVPR 2026 (main)

Abstract: We introduce FlexAvatar, a method for creating high-quality and complete 3D head avatars from a single image. A core challenge lies in the limited availability of multi-view data and the tendency of monocular training to yield incomplete 3D head reconstructions. We identify the root cause of this issue as the entanglement between driving signal and target viewpoint when learning from monocular videos. To address this, we propose a transformer-based 3D portrait animation model with learnable data source tokens, so-called bias sinks, which enables unified training across monocular and multi-view datasets. This design leverages the strengths of both data sources during inference: strong generalization from monocular data and full 3D completeness from multi-view supervision. Furthermore, our training procedure yields a smooth latent avatar space that facilitates identity interpolation and flexible fitting to an arbitrary number of input observations. In extensive evaluations on single-view, few-shot, and monocular avatar creation tasks, we verify the efficacy of FlexAvatar. Many existing methods struggle with view extrapolation while FlexAvatar generates complete 3D head avatars with realistic facial animations.
AnyDoc: Enhancing Document Generation via Large-Scale HTML/CSS Data Synthesis and Height-Aware Reinforcement Optimization
Authors: Jiawei Lin; Wanrong Zhu; Vlad I Morariu; Christopher Tensmeyer
Venue: CVPR 2026 (main)

Abstract: Document generation has gained growing attention in the field of AI-driven content creation. In this work, we push its boundaries by introducing AnyDoc, a framework capable of handling multiple generation tasks across a wide spectrum of document categories, all represented in a unified HTML/CSS format. To overcome the limited coverage and scale of existing human-crafted document datasets, AnyDoc first establishes a scalable data synthesis pipeline to automatically generate documents in HTML/CSS form. This pipeline yields DocHTML, a large-scale dataset containing 265,206 document samples, while spanning 111 categories and 32 distinct styles. Additionally, all documents are equipped with comprehensive metadata, including design intentions, HTML/CSS source code, visual assets, and rendered screenshots. Building on the curated dataset, AnyDoc fine-tunes multi-modal large language models (MLLMs) to achieve three practical document generation tasks: intention-to-document, document derendering, and element-to-document. To address the content overflow issue observed during fine-tuning, AnyDoc further incorporates a height-aware reinforcement learning (HARL) post-training procedure. By defining a reward function based on the difference between predicted and target document heights, overflow is penalized and gradually mitigated during HARL, thereby enhancing overall performance. Qualitative and quantitative experiments demonstrate that AnyDoc outperforms both general-purpose MLLMs and task-specific baselines across all three tasks.
Breaking the 3D Dataset Bottleneck: Fast Scalable Generation of Aligned 3D Assets from Scratch for Category 6D Pose Estimation and Robotic Grasping
Authors: Duret Guillaume; Danylo Mazurak; Florence Zara; Jan Peters; Liming Chen
Venue: CVPR 2026 (main)

Abstract: While 2D vision has been revolutionized by large-scale datasets like ImageNet, 3D vision remains constrained by the scarcity of high-quality, canonically aligned data. We introduce the first scalable, automated framework that generates complete category-level 6D pose datasets directly from text prompts, bypassing the need for existing 3D assets. Our method overcomes key challenges by: (1) ensuring reliable, scalable asset generation via a controlled text-to-image-to-3D pipeline; (2) enforcing built-in canonical alignment through depth-conditioned generation, achieving a 96% pose consistency rate; and (3) enabling large-scale 6D annotation via mixed reality rendering. The pipeline produces high-quality, aligned 3D meshes in under 3 minutes per object--a 5-20x speedup over traditional scanning. We generate over 1,000 instances for each of the 153 categories in the Omni6Dpose benchmark, culminating in 153,000 aligned meshes--a >40x increase in instances per category over previous aligned real-world datasets. Extensive evaluation demonstrates competitive zero-shot sim2real transfer on the NOCS 6D pose benchmark and superior robotic grasping performance in both simulation and real-world zero-shot transfer, where aligned meshes prove essential for success. We release the largest publicly available aligned 3D mesh dataset, largest category-level 6D pose dataset, grasping simulation environments, and open-source pipeline, providing a critical step toward foundation models for 3D understanding and enabling efficient, unlimited generation of task-specific 3D data from scratch. The code and datasets can be found at https://genomni3d.github.io/
Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation
Authors: Pingrui Zhang; Yifei Su; Pengyuan Wu; Dong An; Li Zhang; Zhigang Wang; Dong Wang; Bin Zhao
Venue: CVPR 2026 (main)

Abstract: Vision-and-Language Navigation (VLN) requires the agent to navigate based on natural instructions. This task is challenging due to partial observability, which makes it difficult to align perception with language. Recent methods mitigate this by imagining future scenes, yet they rely on vision-based synthesis, leading to high computational cost and redundant details. To this end, we propose to adaptively imagine key environmental semantics via language form, enabling a more reliable and efficient strategy. Specifically, we introduce Adaptive Text Dreamer (ATD), a dual-branch self-guided imagination policy built upon a large language model (LLM). ATD is designed with a human-like left-right brain architecture, where the left brain focuses on logical integration, and the right brain is responsible for imaginative prediction of future scenes. To achieve this, we fine-tune only the Q-former within both brains to efficiently activate domain-specific knowledge in the LLM, enabling dynamic updates of logical reasoning and imagination during navigation. Furthermore, we propose a cross-interaction mechanism that regularizes the imagined latent-space outputs and integrates them with the navigation expert module via a decoder-free latent interface, thereby enabling ATD to jointly harness the reasoning ability of the LLM and the task-specific knowledge of the navigation model. We conduct extensive experiments across the R2R, REVERIE, and R4R benchmarks, demonstrating that ATD achieves competitive performance with significantly fewer parameters. The code will be available.
The Road Less Seen: Segment Exploration for Weakly Supervised Video Anomaly Detection
Authors: Anusha Acharya; Hitesh Sapkota; Qi Yu; Xumin Liu
Venue: CVPR 2026 (main)

Abstract: Weakly supervised learning (WSL) provides a cost-effective learning paradigm for video anomaly detection (VAD) from data with video-level annotation instead of requiring costly fine-grained segment-level annotation. Although contemporary methods have shown promising results on challenging real-world surveillance videos, most of them are evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC). We reveal that a high AUROC could result in a very low recall for meaningful False Positive Rate (FPR) thresholds. Thus, these models suffer from limited practical values, especially in high-stake domains (e.g. public safety and medical diagnosis), where missing the true anomalies incur high cost. This surprising phenomenon is rooted in the interplay of weak supervision and the highly imbalanced distribution between normal and anomalous video segments. To tackle this key challenge in VAD systems, we propose a novel dual exploration strategy that combines temporal clustering with uncertainty-based segment exploration. Temporal clustering selects diverse segments based on both semantic and temporal similarity, while uncertainty-based sampling targets low-scoring segments with high model uncertainty. The main aim of exploration is to ensure that the model learns from a wide range of patterns, both diverse and ambiguous, resulting in more informed and robust decision-making, and reduction in false negatives. Meanwhile, we adopt two practical metrics to replace the commonly used AUROC score for a more effective measure for evaluation. Experiments conducted in challenging real-world videos demonstrate that our exploration strategy improves VAD performance compared to the baselines on these metrics, which justifies its improved practical value in real-world settings.
SurgCoT: Advancing Spatiotemporal Reasoning in Surgical Videos through a Chain-of-Thought Benchmark
Authors: Gui Wang; YongSong Zhou; Kaijun Deng; Wooi Ping Cheah; Rong Qu; Jianfeng Ren; Linlin Shen
Venue: CVPR 2026 (main)

Abstract: Fine-grained spatiotemporal reasoning on surgical videos is critical, yet the capabilities of Multi-modal Large Language Models (MLLMs) in this domain remain largely unexplored. To bridge this gap, we introduce SurgCoT, a unified benchmark for evaluating chain-of-thought (CoT) reasoning in MLLMs across 7 surgical specialties and 35 diverse procedures. SurgCoT assesses five core reasoning dimensions: Causal Action Ordering, Cue-Action Alignment, Affordance Mapping, Micro-Transition Localization, and Anomaly Onset Tracking, through a structured CoT framework with an intensive annotation protocol (Question-Option-Knowledge-Clue-Answer), where the Knowledge field provides essential background context and Clue provides definitive spatiotemporal evidence. Evaluation of 10 leading MLLMs shows: 1) commercial models outperform open-source and medical-specialized variants; 2) significant gaps exist in surgical CoT reasoning; 3) SurgCoT enables effective evaluation and enhances progressive spatiotemporal reasoning. SurgCoT provides a reproducible testbed to narrow the gap between MLLM capabilities and clinical reasoning demands. Code and data: https://github.com/CVI-SZU/SurgCoT.
Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flow
Authors: Shimin Hu; Yuanyi Wei; Fei Zha; Yudong Guo; Juyong Zhang
Venue: CVPR 2026 (main)

Abstract: Existing 3D editing methods rely on computationally intensive scene-by-scene iterative optimization and suffer from multi-view inconsistency. We propose an effective and feed-forward 3D editing framework based on the TRELLIS generative backbone, capable of modifying 3D models from a single editing view. Our framework addresses two key issues: adapting training-free 2D editing to structured 3D representations, and overcoming the bottleneck of appearance fidelity in compressed 3D features. To ensure geometric consistency, we introduce Voxel FlowEdit, an edit-driven flow in the sparse voxel latent space that achieves globally consistent 3D deformation in a single pass. To restore high-fidelity details, we develop a normal-guided single to multi-view generation module as an external appearance prior, successfully recovering high-frequency textures. Experiments demonstrate that our method enables fast, globally consistent, and high-fidelity 3D model editing.
Scenes as Tokens: Multi-Scale Normal Distributions Transform Tokenizer for General 3D Vision-Language Understanding
Authors: Yutao Tang; Cheng Zhao; Gaurav Mittal; Rohith Kukkala; Rama Chellappa; Cheng Peng; Mei Chen
Venue: CVPR 2026 (main)

Abstract: Recent advances in 3D vision-language models (VLMs) highlight a strong potential for 3D scene understanding and reasoning.However, effectively tokenizing 3D scenes into holistic scene tokens, and leveraging these tokens across diverse 3D understanding tasks, remain highly challenging. We present NDTokenizer3D, a generalist 3D VLM that performs a wide range of 3D scene understanding tasks while naturally supporting human interactions, thereby bridging language-level reasoning with 3D spatial understanding. The core of our approach is a novel three-stage scene tokenization pipeline built upon a Multi-Scale Normal Distributions Transform (NDT) representation, paired with a Multi-Scale NDT Decoder (MSDec).Specifically, NDTokenizer3D first constructs a multi-scale NDT representation from raw high-resolution point clouds, preserving both global context and fine-grained geometric details. Next, the MSDec progressively fuses cross-scale NDT features, producing holistic scene tokens consumable by LLM endpoints. Beyond tokenization, MSDec is repurposed as a general interface for human-interactive prompting (points, boxes, masks) and segmentation-mask decoding, unifying diverse 3D scene understanding tasks within a single architecture. With this compact and unified design, NDTokenizer3D offers a fine-grained, general-purpose 3D VLM, achieving remarkable improvements in 3D Referring Segmentation, 3D Visual Question Answering, and 3D Dense Captioning.