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首次观测 · 记录 #1CVF · CVPR 2026:cvf:051c939fbf9b7209f3462c00d6a505c8CVF · CVPR 2026

Thinking in 360deg: Humanoid Visual Search in the Wild

Yu, Heyang, Han, Yinan, Zhang, Xiangyu, Yin, Baiqiao, Chang, Bowen, Han, Xiangyu, Liu, Xinhao, Zhang, Jing 等 12 位作者

原文摘要

Humans rely on the synergistic control of head (cephalomotor) and eye (oculomotor) to efficiently search for visual information in 360deg. However, prior approaches to visual search are limited to a static image, neglecting the physical embodiment and its interaction with the 3D world. How can we develop embodied visual search agents as efficient as humans while bypassing the constraints imposed by real-world hardware? To this end, we propose humanoid visual search where a humanoid agent actively rotates its head to search for objects or paths in an immersive world represented by a 360deg panoramic image. To study visual search in visually-crowded real-world scenarios, we build H* Bench, a new benchmark that moves beyond household scenes to challenging in-the-wild scenes that necessitate advanced visual-spatial reasoning capabilities, such as transportation hubs, large-scale retail spaces, urban streets, and public institutions. Our experiments first reveal that even top-tier proprietary models falter, achieving only 30% success in object and path search. We then use post-training techniques to enhance the open-source models, increasing its success rate by over threefold for both object search (14.83% - 47.38%) and path search (6.44% - 24.94%) on the smallest 3B model. Notably, the lower ceiling of path search reveals its inherent difficulty, which we attribute to the demand for sophisticated spatial commonsense. Our results not only show a promising path forward but also quantify the immense challenge that remains in building MLLM agents that can be seamlessly integrated into everyday human life.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:19947eae1e7944b631c54b1871228cb2CVF · CVPR 2026

Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models

Zhou, Shengchao, Chen, Yuxin, Ge, Yuying, Huang, Wei, Lin, Jiehong, Shan, Ying, Qi, Xiaojuan

原文摘要

Vision-language models (VLM) excel at general understanding yet remain weak at dynamic spatial reasoning (DSR), i.e., reasoning about the evolvement of object geometry and relationship in 3D space over time, largely due to the scarcity of scalable 4D-aware training resources. To bridge this gap across aspects of dataset, benchmark and model, we introduce DSR Suite. First, we propose an automated pipeline that generates multiple-choice question-answer pairs from in-the-wild videos for DSR. By leveraging modern vision foundation models, the pipeline extracts rich geometric and motion information, including camera poses, local point clouds, object masks, orientations, and 3D trajectories. These geometric cues enable the construction of DSR-Train for learning and further human-refined DSR-Bench for evaluation. Compared with previous works, our data emphasize (i) in-the-wild video sources, (ii) object- and scene-level 3D requirements, (iii) viewpoint transformations, (iv) multi-object interactions, and (v) fine-grained, procedural answers. Beyond data, we propose a lightweight Geometry Selection Module (GSM) to seamlessly integrate geometric priors into VLMs, which condenses question semantics and extracts question-relevant knowledge from pretrained 4D reconstruction priors into a compact set of geometry tokens. This targeted extraction avoids overwhelming the model with irrelevant knowledge. Experiments show that integrating DSR-Train and GSM into Qwen2.5-VL-7B significantly enhances its dynamic spatial reasoning capability, while maintaining accuracy on general video understanding benchmarks.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:22f760450fd95ae42743ff5a75e5a449CVF · CVPR 2026

CubeComposer: Spatio-Temporal Autoregressive 4K 360deg Video Generation from Perspective Video

Li, Lingen, Wang, Guangzhi, Li, Xiaoyu, Zhang, Zhaoyang, Dou, Qi, Gu, Jinwei, Xue, Tianfan, Shan, Ying

原文摘要

Generating high-quality 360deg panoramic videos from perspective input is one of the crucial applications for virtual reality (VR), whereby high-resolution videos are especially important for immersive experience. Existing methods are constrained by computational limitations of vanilla diffusion models, only supporting <= 1K resolution native generation and relying on suboptimal post-hoc super-resolution. We introduce CubeComposer, a novel spatio-temporal autoregressive diffusion model that natively generates 4K-resolution 360deg videos. By decomposing videos into cubemap representations with six faces, CubeComposer autoregressively synthesizes content in a well-planned spatio-temporal order, reducing peak memory demands while enabling high-resolution output. Specifically, to address challenges in multi-dimensional autoregression, we propose: (1) a spatio-temporal autoregressive strategy that orchestrates 360deg video generation across cube faces and time windows for coherent synthesis; (2) a cube face context management mechanism, equipped with a sparse context attention design to improve efficiency; and (3) continuity-aware techniques, including cube-aware positional encoding, padding, and blending to eliminate boundary seams. Extensive experiments on benchmark datasets demonstrate that CubeComposer outperforms state-of-the-art methods in native resolution and visual quality, supporting practical VR application scenarios.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:2501dd915c9a001486063a711d886e4eCVF · CVPR 2026

SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection

Liang, Jiaming, Zhan, Yifeng, Liu, Chunlin, Zheng, Weihua, Peng, Bingye, Liang, Qiwei, Cai, Boyang, Mai, Xiaochun 等 9 位作者

原文摘要

Open-vocabulary object detection (OVOD) aims to detect known and unknown objects in the open world by leveraging text prompts. Benefiting from the emergence of large-scale vision--language pre-trained models, OVOD has demonstrated strong zero-shot generalization capabilities. However, when dealing with camouflaged objects, the detector often fails to distinguish and localize objects because the visual features of the objects and the background are highly similar. To bridge this gap, we construct a benchmark named OVCOD-D by augmenting carefully selected camouflaged object images with fine-grained textual descriptions. Due to the limited scale of available camouflaged object datasets, we adopt detectors pre-trained on large-scale object detection datasets as our baseline methods, as they possess stronger zero-shot generalization ability. In the specificity-aware sub-descriptions generated by multimodal large models, there still exist confusing and overly decorative modifiers. To mitigate such interference, we design a sub-description principal component contrastive fusion strategy that reduces noisy textual components. Furthermore, to address the challenge that the visual features of camouflaged objects are highly similar to those of their surrounding environment, we propose a specificity-guided regional weak alignment and dynamic focusing method, which aims to strengthen the detector's ability to discriminate camouflaged objects from background. Under the open-set evaluation setting, the proposed method achieves an AP of 56.4 on the OVCOD-D benchmark. Project code and benchmark will be released at https://github.com/Zh1fen/SDDF.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:27b23a97712bcd6579727ab3f57ea7e7CVF · CVPR 2026

Sculpt4D: Generating 4D Shapes via Sparse-Attention Diffusion Transformers

Yin, Minghao, Hu, Wenbo, Xu, Jiale, Shan, Ying, Han, Kai

原文摘要

Recent breakthroughs in 3D generative modeling have yielded remarkable progress in static shape synthesis, yet truly dynamic 4D generation remains elusive, hindered by temporal artifacts and prohibitive computational demand. We present Sculpt4D, a native 4D generative framework that seamlessly integrates efficient temporal modeling into a pretrained 3D Diffusion Transformer (Hunyuan3D 2.1), thereby mitigating the scarcity of 4D training data. At its core lies a Block Sparse Attention mechanism that preserves object identity by anchoring to the initial frame while capturing rich motion dynamics via a time-decaying sparse mask. This design faithfully models complex spatiotemporal dependencies with high fidelity, while sidestepping the quadratic overhead of full attention and reducing network total computation by 56%. Consequently, Sculpt4D establishes a new state-of-the-art in temporally coherent 4D synthesis, charting a path toward efficient and scalable 4D generation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:30841996581068fbd80f6cec5966cd58CVF · CVPR 2026

SpatiaLQA: A Benchmark for Evaluating Spatial Logical Reasoning in Vision-Language Models

Xie, Yuechen, Zhang, Xiaoyan, Shan, Yicheng, Hao, Zhu, Tang, Rui, Wei, Rong, Song, Mingli, Wan, Yuanyu 等 9 位作者

原文摘要

Vision-Language Models (VLMs) have been increasingly applied in real-world scenarios due to their outstanding understanding and reasoning capabilities. Although VLMs have already demonstrated impressive capabilities in common visual question answering and logical reasoning, they still lack the ability to make reasonable decisions in complex real-world environments. We define this ability as spatial logical reasoning, which not only requires understanding the spatial relationships among objects in complex scenes, but also the logical dependencies between steps in multi-step tasks. To bridge this gap, we introduce Spatial Logical Question Answering (SpatiaLQA), a benchmark designed to evaluate the spatial logical reasoning capabilities of VLMs. SpatiaLQA consists of 9,605 question answer pairs derived from 241 real-world indoor scenes. We conduct extensive experiments on 41 mainstream VLMs, and the results show that even the most advanced models still struggle with spatial logical reasoning. To address this issue, we propose a method called recursive scene graph assisted reasoning, which leverages visual foundation models to progressively decompose complex scenes into task-relevant scene graphs, thereby enhancing the spatial logical reasoning ability of VLMs, outperforming all previous methods. We will release our code and dataset soon.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3dbfaf5e3a5fba35abfc9565544850a8CVF · CVPR 2026

Building a Precise Video Language with Human-AI Oversight

Lin, Zhiqiu, Cen, Siyuan, Mitra, Chancharik, Li, Isaac, Huang, Yuhan, Ling, Yu Tong Tiffany, Wang, Hewei, Pi, Irene 等 12 位作者

原文摘要

Video-language models (VLMs) learn to reason about the dynamic visual world through natural language. We introduce a suite of open datasets, benchmarks, and recipes for scalable oversight that enable precise video captioning. First, we define a structured specification for describing subjects, scenes, motion, spatial, and camera dynamics, grounded by hundreds of carefully defined visual primitives developed with professional video creators such as filmmakers. Next, to curate high-quality captions, we introduce a critique-based human-AI (CHAI) oversight framework, where trained human experts provide correctional critiques to revise model-generated pre-captions into improved post-captions. This division of labor improves annotation accuracy and efficiency by offloading text generation to models, allowing humans to better focus on verification. Additionally, these critiques and preferences between pre- and post-captions provide rich supervision for improving open-source models (Qwen3-VL) on caption generation, reward modeling, and critique generation through SFT, DPO, and inference-time scaling. Our ablations show that critique quality in precision, recall, and constructiveness, ensured by our oversight framework, directly governs downstream performance. With modest expert supervision, the resulting model outperforms even closed-source models such as Gemini-3.1-Pro. Finally, we apply our approach to re-caption large-scale professional videos (e.g., films, commercials, games) and fine-tune video generation models such as Wan to better follow detailed prompts of up to 400 words, achieving finer control over cinematography including camera motion, angle, lens, focus, point of view, and framing. Overall, our results show that precise specification and human-AI oversight are key to achieving professional-level video understanding and generation.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:61a4abc798f83631aaffc25df6e38817CVF · CVPR 2026

DF^2-VB: Dual-level Fuzzy Fusion with View-specific Boosting for Multi-view Multi-label Classification

Lin, Yuena, Cai, Haichun, Shan, Yi, Wei, Hao, Deng, Yongjian, Yang, Zhen, Lyu, Gengyu

原文摘要

Multi-view multi-label classification (MVMLC) aims to utilize both consensus and complementarity information to predict potentially relevant labels for samples. Existing MVMLC approaches typically focus on either feature-level fusion, which integrates complementary features for more expressive representations, or decision-level fusion, which aggregates view-specific predictions to exploit label supervision more effectively. In fact, relying solely on feature-level fusion often underutilizes label information and limits discriminability of learned representations, whereas pure decision-level fusion pays insufficient attention to view representation expressiveness and thus constrains classification performance. To address these limitations, we propose DF^2-VB, a dual-level fusion framework that jointly exploits complementary strengths to mitigate their respective weaknesses by integrating feature- and decision-level fusion. At the feature level, a Fuzzy Dynamic Fusion (FDF) module maps consensus features into a more compatible fuzzy feature space, where essential features are identified and redundant features are suppressed to further fuse an expressive consensus representation and boost view-specific predictions for decision-level fusion. At the decision level, a View-specific Boosting (VB) strategy adaptively measures the importance of samples and view-specific predictions to strengthen the utilization of supervision for facilitating the discriminability in feature-level fusion. Complementarily, FDF and VB jointly reinforce the model expressiveness and discriminability for reliable predictions. Extensive experiments on multiple public datasets verify the superiority of our strategy over advanced MVMLC models.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:65dbc90a9901c515ee499113b19e6112CVF · CVPR 2026

Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images

Liu, Hongyuan, Zou, Bochao, Liu, Qiankun, Yu, Haochen, Mei, Qi, Jiang, Jianfei, Liu, Chen, Bi, Cheng 等 13 位作者

原文摘要

Creating realistic and simulation-ready 3D assets is crucial for autonomous driving research and virtual environment construction. However, existing 3D vehicle generation methods are often trained on synthetic data with significant domain gaps from real-world distributions. The generated models often exhibit arbitrary poses and undefined scales, resulting in poor visual consistency when integrated into driving scenes. In this paper, we present Unposed-to-3D, a novel framework that learns to reconstruct 3D vehicles from real-world driving images using image-only supervision. Our approach consists of two stages. In the first stage, we train an image-to-3D reconstruction network using posed images with known camera parameters. In the second stage, we remove camera supervision and use a camera prediction head that directly estimates the camera parameters from unposed images. The predicted pose is then used for differentiable rendering to provide self-supervised photometric feedback, enabling the model to learn 3D geometry purely from unposed images. To ensure simulation readiness, we further introduce a scale-aware module to predict real-world size information, and a harmonization module that adapts the generated vehicles to the target driving scene with consistent lighting and appearance. Extensive experiments demonstrate that Unposed-to-3D effectively reconstructs realistic, pose-consistent, and harmonized 3D vehicle models from real-world images, providing a scalable path toward creating high-quality assets for driving scene simulation and digital twin environments.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:7e3cde56560b107b54a9250094fc98d6CVF · CVPR 2026

DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving

Ma, Enhui, Zhang, Jiahuan, Zheng, Guantian, Tang, Tao, Li, Shengbo Eben, Lu, Yuhang, Zhou, Xia, Zhang, Xueyang 等 13 位作者

原文摘要

Multimodal Large Language Models (MLLMs) are rapidly becoming the intelligence brain of end-to-end autonomous driving systems. A key challenge is to assess whether MLLMs can truly understand and follow complex real-world traffic rules. However, existing benchmarks mainly focus on single-rule scenarios like traffic sign recognition, neglecting the complexity of multi-rule concurrency and conflicts in real driving. Consequently, models perform well on simple tasks but often fail or violate rules in real world complex situations. To bridge this gap, we propose DriveCombo, a text and vision-based benchmark for compositional traffic rule reasoning. Inspired by human drivers' cognitive development, we propose a systematic Five-Level Cognitive Ladder that evaluates reasoning from single-rule understanding to multi-rule integration and conflict resolution, enabling quantitative assessment across cognitive stages. We further propose a Rule2Scene Agent that maps language-based traffic rules to dynamic driving scenes through rule crafting and scene generation, enabling scene-level traffic rule visual reasoning. Evaluations of 14 mainstream MLLMs reveal a pronounced decline in performance as task difficulty increases, especially in the rule conflict resolution task. After splitting the dataset and fine-tuning on the training set, we further observe substantial improvements in both traffic rule reasoning and downstream planning capabilities. These results highlight the effectiveness of DriveCombo in advancing compliant and intelligent autonomous driving systems.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8573dd2e768d452ce0623092daf4147fCVF · CVPR 2026

Beyond Reassembly: Fractured Object Recovery with Missing Parts

Xu, Qun-Ce, Li, Jiahui, Cao, Yan-Pei, Cheng, Weihao, Mu, Tai-Jiang, Shan, Ying, Li, Chuan, Chen, Da 等 10 位作者

原文摘要

We propose a novel learning-based task named fractured object recovery. Unlike the previous fractured object reassembly task that only aligns existing parts with overlaps, our task aims to recover the complete shape by not only reassembling irrelevant parts but also predicting missing parts. Our task coincides with practical experiences, where the prior knowledge of similar shapes can be leveraged such that even non-overlapping parts can be reasoned into adequate locations. We also present the first learning model for the proposed task by correlating features of both existing and missing parts using a transformer, where the latter is naturally represented as missing tokens. To facilitate the task, we introduce a new dataset based on the existing fractured object benchmark by imposing different configurations of missing parts. We perform extensive evaluations to demonstrate the performance of the proposed model over baselines. The results show that joint part reassembly and prediction can be made possible and also have mutual benefits, which we believe can inspire future research and favor real applications.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:91dffe9a14d08c49bf7147ae61b41cddCVF · CVPR 2026

PAF: Perturbation-Aware Filtering for Open-Set Semi-Supervised Learning

Han, Yinan, Jiang, Qing-Yuan

原文摘要

Open-set semi-supervised learning (OSSL) has achieved notable progress in exploiting unlabeled data, yet most existing methods overlook the distinct sensitivities of in-distribution (ID) and out-of-distribution (OOD) samples to semantic-preserving perturbations, resulting in suboptimal model performance. To address this limitation, we propose Perturbation-Aware Filtering (PAF), which leverages the behavioral difference between ID and OOD samples under perturbations and extends it into a representation-level signal for reliable OOD filtering. Specifically, PAF identifies OOD samples by measuring the representation instability under semantic-preserving perturbations. We then integrate PAF into a carefully designed two-stage training framework, allowing the model to exploit abundant unlabeled data in the first stage and gradually adapt to the open-set setting with limited labels in the second stage. Extensive experimental results on widely-used OSSL benchmarks demonstrate that our proposed PAF approach achieves superior performance compared to state-of-the-art (SOTA) OSSL methods. Our code is available at https://github.com/njustkmg/CVPR26-PAF.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:92a82b887d476e26aaefd94d38cc2216CVF · CVPR 2026

MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE

Zhu, Ruijie, Lu, Jiahao, Hu, Wenbo, Han, Xiaoguang, Cai, Jianfei, Shan, Ying, Zheng, Chuanxia

原文摘要

We present MotionCrafter, a framework that leverages video generators to jointly reconstruct 4D geometry and estimate dense motion from a monocular video. The key idea is a joint representation of dense 3D point maps and 3D scene flows in a shared coordinate system, together with a 4D VAE tailored to learn this representation effectively. Unlike prior work that strictly aligns 3D values and latents with RGB VAE latents--despite their fundamentally different distributions--we show that such alignment is unnecessary and can hurt performance. Instead, we propose a new data normalization and VAE training strategy that better transfers diffusion priors and greatly improves reconstruction quality. Extensive experiments on multiple datasets show that MotionCrafter achieves state-of-the-art performance in both geometry reconstruction and dense scene flow estimation, delivering 38.64% and 25.0% improvements in geometry and motion reconstruction, respectively, all without any post-optimization.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:9f96ce3cd90e3fc80830cb93d30df1fdCVF · CVPR 2026

SaPaVe: Towards Active Perception and Manipulation in Vision-Language Action Models for Robotics

Liu, Mengzhen, Zhou, Enshen, Chi, Cheng, Han, Yi, Rong, Shanyu, Chen, Liming, Wang, Pengwei, Wang, Zhongyuan 等 9 位作者

原文摘要

Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven perception actively with robust, viewpoint-invariant execution accordingly. To this end, we propose SaPaVe, an end-to-end framework that jointly learns these capabilities in a data-efficient manner. Central to our approach is a decoupling of camera and manipulation actions, contrary to shared-action-space, and learning in a bottom-up strategy: we first train semantic camera control on our proposed large-scale dataset, then jointly optimizes both action types via hybrid data. To support this, we introduce ActiveViewPose-200K, comprising 200k image-language-camera movement pairs for semantic camera movement learning, and a 3D geometry-aware module that improves execution robustness under dynamic viewpoints. We further present ActiveManip-Bench, the first benchmark filling the gap to evaluate active manipulation. Extensive experiments in both simulation and real-world settings show that SaPaVe outperforms recent VLA models such as GR00T N1 and pi0, achieving up to 31.25% higher success rates in real-world tasks. Our results show that tightly coupled perception and execution, when trained with decoupled yet coordinated strategies, enable efficient and generalizable active manipulation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:a10e5b9dac857de746a71643b1c93cb0CVF · CVPR 2026

MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation

Xu, Jiale, Zhao, Wang, Shan, Ying

原文摘要

Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next vertex instead of independent coordinates. At its core is a multi-level sparse-voxel encoder that injects geometric context into the generative process in three complementary ways: providing voxel features as vertex representations, guiding token prediction via cross-attention to voxel features, and serving as a structural scaffold that constrains generation around the input surface. Our hierarchical design enables coarse-to-fine vertex prediction in a single decoding step, while tightly coupling the generative model with 3D geometry. Extensive experiments demonstrate that MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:a4533a79d1a62ba12eafcc36304c3794CVF · CVPR 2026

VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control

Zheng, Sixiao, Yin, Minghao, Hu, Wenbo, Li, Xiaoyu, Shan, Ying, Fu, Yanwei

原文摘要

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently capture dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a geometry-driven video world model that generates dynamic, realistic videos from a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state as a static background point cloud and per-object 3D Gaussian trajectories. This representation captures each object's motion path and probabilistic 3D occupancy over time, providing a flexible, category-agnostic alternative to rigid bounding boxes and parametric models. We render 4D Geometric Control into 4D control maps for a pretrained video diffusion model, enabling high-fidelity, view-consistent video generation that faithfully follows the specified dynamics. To enable training at scale, we develop an automatic data engine and construct VerseControl4D, a real-world dataset of 35K training samples with automatically derived prompts and rendered 4D control maps. Extensive experiments show that VerseCrafter achieves superior visual quality and more accurate control over camera and multi-object motion than prior methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf9f770951ef2d08c4df34d03c7d909cCVF · CVPR 2026

SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks

Shan, Yimeng, Ren, Zhenbang, Wu, Haodi, Wei, Wenjie, Zhu, Rui-Jie, Wang, Shuai, Zhang, Dehao, Xiao, Yichen 等 14 位作者

原文摘要

Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based Spike-Driven Tracking (SDTrack) pipeline. It incorporates a novel event frame aggregation method called Global Trajectory Prompt (GTP) and a Transformer-based tracker. The GTP method effectively captures global trajectory information and aggregates it with event streams into event frames to enhance spatiotemporal representation. The Transformer-based tracker comprises a fully spike-driven SNN backbone and a simple tracking head. The SDTrack pipeline operates end-to-end without data augmentation or post-processing. Extensive experiments demonstrate that our SDTrack-Tiny pipeline achieves competitive accuracy with only 19.61M parameters and 8.16mJ energy consumption, while our Base version achieves state-of-the-art accuracy across three datasets. Our work establishes a solid foundation for future neuromorphic vision research.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e5a00cdb4924b9689234d441454c67c5CVF · CVPR 2026

Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising

Shan, Yiwen, Zhao, Haiyu, Hu, Peng, Peng, Xi, Gou, Yuanbiao

原文摘要

Self-supervised real-world image denoising remains a fundamental challenge, arising from the antagonistic trade-off between decorrelating spatially structured noise and preserving high-frequency details. Existing blind-spot network (BSN) methods rely on pixel-shuffle downsampling (PD) to decorrelate noise, but aggressive downsampling fragments fine structures, while milder downsampling fails to remove correlated noise. To address this, we introduce Next-Scale Prediction (NSP), a novel self-supervised paradigm that decouples noise decorrelation from detail preservation. NSP constructs cross-scale training pairs, where BSN takes low-resolution, fully decorrelated sub-images as input to predict high-resolution targets that retain fine details. As a by-product, NSP naturally supports super-resolution of noisy images without retraining or modification. Extensive experiments demonstrate that NSP achieves state-of-the-art self-supervised denoising performance on real-world benchmarks, significantly alleviating the long-standing conflict between noise decorrelation and detail preservation. The code is available at https://github.com/XLearning-SCU/2026-CVPR-NSP.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:fcc5cd9ad49bd3b0043e1ef11ae9e540CVF · CVPR 2026

Motion-Aware Animatable Gaussian Avatars Deblurring

Niu, Muyao, Zhan, Yifan, Zhu, Qingtian, Li, Zhuoxiao, Wang, Wei, Zhong, Zhihang, Sun, Xiao, Zheng, Yinqiang

原文摘要

The creation of 3D human avatars from multi-view videos is a significant yet challenging task in computer vision. However, existing techniques rely on high-quality, sharp images as input, which are often impractical to obtain in real-world scenarios due to variations in human motion speed and intensity. This paper introduces a novel method for directly reconstructing sharp 3D human Gaussian avatars from blurry videos. The proposed approach incorporates a 3D-aware, physics-based model of blur formation caused by human motion, together with a 3D human motion model designed to resolve ambiguities in motion-induced blur. This framework enables the joint optimization of the avatar representation and motion parameters from a coarse initialization. Comprehensive benchmarks are established using both a synthetic dataset and a real-world dataset captured with a 360-degree synchronous hybrid-exposure camera system. Extensive evaluations demonstrate the effectiveness of the model across diverse conditions. Codes Available: https://github.com/MyNiuuu/MAD-Avatar

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗