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首次观测 · 记录 #1CVF · CVPR 2026:cvf:10843d50034ec7ba86f863d3abb22893CVF · CVPR 2026

CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning

Yang, Jiange, Shi, Yansong, Zhu, Haoyi, Liu, Mingyu, Ma, Kaijing, Wang, Yating, Wu, Gangshan, He, Tong 等 9 位作者

原文摘要

Unsupervised learning of latent motion from Internet videos is crucial for robot learning. Existing discrete methods generally mitigate the shortcut learning caused by extracting excessive static backgrounds through vector quantization with a small codebook size. However, they suffer from information loss and struggle to capture more complex and fine-grained dynamics. Moreover, there is an inherent gap between the distribution of discrete latent motion and continuous robot action, which hinders the joint learning of a unified policy. We propose CoMo, which aims to learn more precise continuous latent motion from internet-scale videos. CoMo employs an early temporal difference (Td) mechanism to increase the shortcut learning difficulty and explicitly enhance motion cues. Additionally, to ensure latent motion better captures meaningful foregrounds, we further propose a temporal contrastive learning (Tcl) scheme. Specifically, positive pairs are constructed with a small future frame temporal offset, while negative pairs are formed by directly reversing the temporal direction. The proposed Td and Tcl work synergistically and effectively ensure that the latent motion focuses better on the foreground and reinforces motion cues. Critically, CoMo exhibits strong zero-shot generalization, enabling it to generate effective pseudo action labels for unseen videos. Extensive simulated and real-world experiments show that policies co-trained with CoMo pseudo action labels achieve superior performance with both diffusion and auto-regressive architectures. The code is available at https://github.com/MCG-NJU/CoMo.

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:1421783c9a499f056fd23a85b01fa2e4CVF · CVPR 2026

TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration

Zhu, Haowei, Huang, Tingxuan, Wang, Xing, Zhao, Tianyu, Wang, Jiexi, Chen, Weifeng, Peng, Xurui, Chen, Fangmin 等 10 位作者

原文摘要

Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (TAP), a training-free, probe-driven framework that adaptively selects a predictor for each token at every sampling step. TAP uses a single full evaluation of the model's first layer as a low-cost probe to compute proxy losses for a compact family of candidate predictors (instantiated primarily with Taylor expansions of varying order and horizon), then assigns each token the predictor with the smallest proxy error. This per-token "probe-then-select" strategy exploits heterogeneous temporal dynamics, requires no additional training, and is compatible with various predictor designs. TAP incurs negligible overhead while enabling large speedups with little or no perceptual quality loss. Extensive experiments across multiple diffusion architectures and generation tasks show that TAP substantially improves the accuracy-efficiency frontier compared to fixed global predictors and caching-only baselines.

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:17f0e08b652869d4dd2845ad10c42ed4CVF · CVPR 2026

Hierarchical Action Learning for Weakly-Supervised Action Segmentation

Huang, Junxian, Cai, Ruichu, Fang, Juntao, Zhu, Hao, Xu, Boyan, Chen, Weilin, Li, Zijian, Gao, Shenghua

原文摘要

Humans perceive actions through key transitions that structure actions across multiple abstraction levels, whereas machines, relying on visual features, tend to over-segment. This highlights the difficulty of enabling hierarchical reasoning in video understanding. Interestingly, we observe that lower-level visual and high-level action latent variables evolve at different rates, with low-level visual variables changing rapidly, while high-level action variables evolve more slowly, making them easier to identify. Building on this insight, we propose the Hierarchical Action Learning (HAL) model for weakly-supervised action segmentation. Our approach introduces a hierarchical causal data generation process, where high-level latent action govern the dynamics of low-level visual features. To model these varying timescales effectively, we introduce deterministic processes to align these latent variables over time. The HAL model employs a hierarchical pyramid transformer to capture both visual features and latent variables, and a sparse transition constraint is applied to enforce the slower dynamics of high-level action variables. This mechanism enhances the identification of these latent variables over time. Under mild assumptions, we prove that these latent action variables are strictly identifiable. Experimental results on several benchmarks show that the HAL model significantly outperforms existing methods for weakly-supervised action segmentation, confirming its practical effectiveness in real-world 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:2ea9a90796146609abafab43e28cf455CVF · CVPR 2026

PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion

Yang, Yichen, Li, Hong, Zhu, Haodong, Yang, Linin, Lei, Guojun, Xu, Sheng, Zhang, Baochang

原文摘要

Existing autoregressive (AR) methods for generating artist-designed meshes struggle to balance global structural consistency with high-fidelity local details, and are susceptible to error accumulation. To address this, we propose PartDiffuser, a novel semi-autoregressive diffusion framework for point-cloud-to-mesh generation. The method first performs semantic segmentation on the mesh and then operates in a "part-wise" manner: it employs autoregression between parts to ensure global topology, while utilizing a parallel discrete diffusion process within each semantic part to precisely reconstruct high-frequency geometric features. PartDiffuser is based on the DiT architecture and introduces a part-aware cross-attention mechanism, using point clouds as hierarchical geometric conditioning to dynamically control the generation process, thereby effectively decoupling the global and local generation tasks. Experiments demonstrate that this method significantly outperforms state-of-the-art (SOTA) models in generating 3D meshes with rich detail, exhibiting exceptional detail representation suitable for real-world 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:313f612daace122adf5a82e21b1096f7CVF · CVPR 2026

UFO: Unifying Feed-Forward and Optimization-based Methods for Large Driving Scene Modeling

Tan, Kaiyuan, Shen, Yingying, Zhu, Ziyue, Tu, Mingfei, Zhu, Haohui, Sun, Haiyang, Wang, Bing, Chen, Guang 等 9 位作者

原文摘要

Dynamic driving scene modeling is critical for autonomous driving simulation and closed-loop learning. While recent feed-forward methods offer fast inference through data-driven priors, they struggle with long-range driving sequences due to quadratic complexity in sequence length and restrictive assumptions for dynamic objects. We propose UFO, a novel recurrent paradigm that combines the strengths of optimization-based and feed-forward methods for efficient long-range 4D modeling. Our approach maintains a persistent 4D scene representation composed of scene tokens that are progressively refined as new frames arrive, enabling future observations to correct earlier uncertain predictions. A visibility-based filtering mechanism exploits the locality of 3D-to-pixel correspondence, reducing complexity from quadratic to near-linear in sequence length. For dynamic objects, we introduce object pose-guided modeling with learned lifespans, enabling complex long-range motion without kinematic assumptions. Experiments on the Waymo Open Dataset demonstrate that UFO significantly outperforms both per-scene optimization and feed-forward baselines across 2s, 8s, and zero-shot 16s sequences.

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:385de013b39e180a553e0f8fc7758517CVF · CVPR 2026

UIKA: Fast Universal Head Avatar from Pose-Free Images

Wu, Zijian, Zhou, Boyao, Hu, Liangxiao, Liu, Hongyu, Sun, Yuan, Wang, Xuan, Cao, Xun, Shen, Yujun 等 9 位作者

原文摘要

We present UIKA, a feed-forward animatable Gaussian head model from an arbitrary number of pose-free inputs, including a single image, multi-view captures, and smartphone-captured videos. Unlike the traditional avatar method, which requires a studio-level multi-view capture system and reconstructs a human-specific model through a long-time optimization process, we rethink the task through the lenses of model representation, network design, and data preparation. First, we introduce a UV-guided avatar modeling strategy, in which each input image is associated with a pixel-wise facial correspondence estimation. Such correspondence estimation allows us to reproject each valid pixel color from screen space to UV space, which is independent of camera pose and character expression. Furthermore, we design learnable UV tokens on which the attention mechanism can be applied at both the screen and UV levels. The learned UV tokens can be decoded into canonical Gaussian attributes using aggregated UV information from all input views. To train our large avatar model, we additionally prepare a large-scale, identity-rich synthetic training dataset. Our method significantly outperforms existing approaches in both monocular and multi-view settings. See more details in our project page: https://zijian-wu.github.io/uika-page/.

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:3ee5db124c653ade18d850e11e0c1874CVF · CVPR 2026

DeX-Portrait: Disentangled and Expressive Portrait Animation via Explicit and Latent Motion Representations

Shi, Yuxiang, Li, Zhe, Wang, Yanwen, Zhu, Hao, Cao, Xun, Liu, Ligang

原文摘要

Portrait animation from a single source image and a driving video is a long-standing problem. Recent approaches tend to adopt diffusion-based image/video generation models for realistic and expressive animation. However, none of these diffusion models realizes high-fidelity disentangled control between the head pose and facial expression, hindering applications like expression-only or pose-only editing and animation. To address this, we propose DeX-Portrait, a novel approach capable of generating expressive portrait animation driven by disentangled pose and expression signals. Specifically, we represent the pose as an explicit global transformation and the expression as an implicit latent code. First, we design a powerful motion trainer to learn both pose and expression encoders for extracting precise and decomposed driving signals. Then we propose to inject the pose transformation into the diffusion model through a dual-branch conditioning mechanism, and the expression latent through cross attention. Finally, we design a progressive hybrid classifier-free guidance for more faithful identity consistency. Experiments show that our method outperforms state-of-the-art baselines on both animation quality and disentangled controllability.

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:7d9903874efd513a639c2c31e1e05e16CVF · CVPR 2026

TANGO: Learning Distribution-wise Foundation Prior Consistency and Instance-wise Style Calibration for Medical Image Generalization

Liu, Chuang, Cao, Yichao, Su, Xiu, Zhu, Haogang

原文摘要

Test-time adaptation (TTA) has emerged as a promising solution to address real world domain shifts in medical image segmentation. Current approaches adapt by updating or regularizing a pre-trained source model. However, they face two major issues: (i) the source models on which they rely are prone to overfitting under domain shifts; (ii) in dynamic continual testing scenarios, error accumulation and class forgetting are further exacerbated. To overcome these limitations, we propose TanGo, a novel framework that combines Training to adapt with Foundation Guidance and continual style calibration. During training, TanGo learns generalization priors from vision foundation models (VFMs) through distribution-wise consistency learning. We incorporate stable low-frequency representations from a frozen encoder of VFMs as priors to guide the source model, constraining its output feature distribution to yield a more generalizable feature space. At test time, we propose an instance-wise style calibration method that uses a learnable data decorator to shift dynamic test images back toward an enhanced source-like distribution. Subsequently, a series of constraints is applied to draw the decorated test samples closer to the enhanced source distribution while maintaining their semantic integrity, thus boosting the continual adaptability of the source model. Extensive experiments on multiple medical image segmentation tasks demonstrate that TanGo achieves state-of-the-art performance.

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:91e669b80d86e3e5235753ed4142e622CVF · CVPR 2026

CrowdGaussian: Reconstructing High-Fidelity 3D Gaussians for Human Crowd from a Single Image

Song, Yizheng, Zhuang, Yiyu, Xu, Qipeng, Wang, Haixiang, Zhu, Jiahe, Tian, Jing, Zhu, Siyu, Zhu, Hao

原文摘要

Single-view 3D human reconstruction has garnered significant attention in recent years. Despite numerous advancements, prior research has concentrated on reconstructing 3D models from clear, close-up images of individual subjects, often yielding subpar results in the more prevalent multi-person scenarios. Reconstructing 3D human crowd models is a highly intricate task, laden with challenges such as: 1) extensive occlusions, 2) low clarity, and 3) numerous and various appearances. To address this task, we propose CrowdGaussian, a unified framework that directly reconstructs multi-person 3D Gaussian Splatting (3DGS) representations from single-image inputs. To handle occlusions, we devise a self-supervised adaptation pipeline that enables the pretrained large human model to reconstruct complete 3D humans with plausible geometry and appearance from heavily occluded inputs. Furthermore, we introduce Self-Calibrated Learning (SCL). This training strategy enables single-step diffusion models to adaptively refine coarse renderings to optimal quality by blending identity-preserving samples with clean/corrupted image pairs. The outputs can be distilled back to enhance the quality of multi-person 3DGS representations. Extensive experiments demonstrate that CrowdGaussian generates photorealistic, geometrically coherent reconstructions of multi-person scenes.

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:b5192df8a3e81e36e001c95b95384430CVF · CVPR 2026

Hierarchically Robust Zero-shot Vision-language Models

Dong, Junhao, Zhang, Yifei, Zhu, Hao, Ong, Yew-Soon, Koniusz, Piotr

原文摘要

Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks. While robust fine-tuning improves their robustness, existing approaches align fixed text embeddings with an image embedding, sacrificing natural performance and robustness. A robustness degradation also occurs when a model faces adversarial attacks targeting superclasses (parent classes, e.g., mammal) in addition to their base (leaf) classes (e.g., cat). Thus, to enhance adversarial robustness and leverage the inherent hierarchical properties of class space, we propose a novel adversarial fine-tuning framework based on hierarchical embeddings and several levels of adversarially robust alignment of image-text modalities. Additional mechanisms place visual embeddings at the desired depth of hierarchy, and we provide a theoretical connection between the depth of embedding in the hierarchy and the maximum viable margin size. Our model naturally realizes several margin sizes, boosting generalization of adversaries for robustification. As various trees with different parent labels can share the same leaf labels, we also consider aligning over multiple trees to boost semantic variety. Experiments across several datasets are performed.

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:ced7109d62bb4514d4670f869e6899c8CVF · CVPR 2026

Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving

Han, Jianhua, Tian, Meng, Zhu, Jiangtong, He, Fan, Zhang, Huixin, Guo, Sitong, Zhu, Dechang, Tang, Hao 等 19 位作者

原文摘要

Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex interactions. However, current vision-language models are weak at spatial grounding and understanding, and VLA systems built on them therefore show limited perception and localization ability.To address these challenges, we introduce Percept-WAM, a perception-enhanced World-Awareness-Action Model that is the first to implicitly integrate 2D/3D scene understanding abilities within a single vision-language model (VLM).Instead of relying on QA-style spatial reasoning, Percept-WAM unifies 2D/3D perception tasks into World-PV and World-BEV tokens, which encode both spatial coordinates and confidence.We propose a grid-conditioned prediction mechanism for dense object perception, incorporating IoU-aware scoring and parallel autoregressive decoding, improving stability in long-tail, far-range, and small-object scenarios. Additionally, Percept-WAM leverages pretrained VLM parameters to retain general intelligence (e.g., logical reasoning) and can output perception results and trajectory control outputs directly. Experiments show that Percept-WAM matches or surpasses classical detectors and segmenters on downstream perception benchmarks, achieving 51.7/58.9 mAP on COCO 2D detection and nuScenes BEV 3D detection. When integrated with trajectory decoders, it further improves planning performance on nuScenes and NAVSIM, e.g., surpassing DiffusionDrive by 2.1 in PMDS on NAVSIM.Qualitative results further highlight its strong open-vocabulary and long-tail generalization.

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:d26e740c49ff30328c06b76ba7974926CVF · CVPR 2026

Pressure2Motion: Hierarchical Human Motion Reconstruction from Ground Pressure with Text Guidance

Li, Zhengxuan, Yang, Qinhui, Zhuang, Yiyu, Guo, Chuan, Zuo, Xinxin, Long, Xiaoxiao, Yao, Yao, Cao, Xun 等 10 位作者

原文摘要

We present Pressure2Motion, a novel motion capture algorithm that reconstructs human motion from a ground pressure sequence and text prompt. At inference time, Pressure2Motion requires only a pressure mat, eliminating the need for specialized lighting setups, cameras, or wearable devices, making it suitable for privacy-preserving, low-light, and low-cost motion capture scenarios. Such a task is severely ill-posed due to the indeterminacy of pressure signals with respect to full-body motion. To address this issue, we introduce Pressure2Motion, a generative model that leverages pressure features as input and utilizes a text prompt as a high-level guiding constraint to resolve ambiguities. Specifically, our model adopts a dual-level feature extractor to accurately interpret pressure data, followed by a hierarchical diffusion model that discerns broad-scale movement trajectories and subtle posture adjustments. Both the physical cues gained from the pressure sequence and the semantic guidance derived from descriptive texts are leveraged to guide the motion estimation with precision. To the best of our knowledge, Pressure2Motion is a pioneering work in leveraging both pressure data and linguistic priors for motion reconstruction, and the established MPL benchmark is the first benchmark for this novel motion capture task. Experiments show that our method generates high-fidelity, physically plausible motions, establishing a new state of the art for this task. The codes and benchmarks will be publicly released upon publication.

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:d7859628d82289a030cd62c0c71ce456CVF · CVPR 2026

TEXTRIX: Latent Attribute Grid for Native Texture Generation and Beyond

Zeng, Yifei, Bao, Yajie, Qian, Jiachen, Wu, Shuang, Lin, Youtian, Zhu, Hao, Li, Buyu, Zhang, Feihu 等 10 位作者

原文摘要

Prevailing 3D texture generation methods, which often rely on multi-view fusion, are frequently hindered by inter-view inconsistencies and incomplete coverage of complex surfaces, limiting the fidelity and completeness of the generated content. To overcome these challenges, we introduce TEXTRIX, a native 3D attribute generation framework for high-fidelity texture synthesis and downstream applications such as precise 3D part segmentation. Our approach constructs a latent 3D attribute grid and leverages a Diffusion Transformer equipped with sparse attention, enabling direct coloring of 3D models in volumetric space and fundamentally avoiding the limitations of multi-view fusion. Built upon this native representation, the framework naturally extends to high-precision 3D segmentation by training the same architecture to predict semantic attributes on the grid. Extensive experiments demonstrate state-of-the-art performance on both tasks, producing seamless, high-fidelity textures and accurate 3D part segmentation with precise boundaries.

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:d81f367d618b04b84238319819767c41CVF · CVPR 2026

SpatialVID: A Large-Scale Video Dataset with Spatial Annotations

Wang, Jiahao, Yuan, Yufeng, Zheng, Rujie, Lin, Youtian, Gao, Jian, Chen, Lin-Zhuo, Bao, Yajie, Zeng, Chang 等 14 位作者

原文摘要

Significant progress has been made in spatial intelligence, spanning both spatial reconstruction and world exploration. However, the scalability and real-world fidelity of current models remain severely constrained by the scarcity of large-scale, high-quality training data. While several datasets provide camera pose information, they are typically limited in scale, diversity, and annotation richness, particularly for real-world dynamic scenes with ground-truth camera motion.To this end, we collect **SpatialVID**, a dataset consisting of a large corpus of in-the-wild videos with diverse scenes, camera movements and dense 3D annotations such as per-frame camera poses, depth, and motion instructions.Specifically, we collect more than 21,000 hours of raw video, and process them into 2.7 million clips through a hierarchical filtering pipeline, totaling 7,089 hours of dynamic content. A subsequent annotation pipeline enriches these clips with detailed spatial and semantic information, including camera poses, depth maps, dynamic masks, structured captions, and serialized motion instructions.Analysis of SpatialVID's data statistics reveals a richness and diversity that directly foster improved model generalization and performance, establishing it as a key asset for the video and 3D vision research community.Through extensive validation experiments, we demonstrate SpatialVID's effectiveness across tasks such as controllable video generation, world simulation and geometric reconstruction, providing a strong foundation for spatial intelligence 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:ebe9e4c87ed802fc1deb2f9f04edadb3CVF · CVPR 2026

Bringing Your Portrait to 3D Presence

Zhang, Jiawei, Chu, Lei, Li, Jiahao, Zang, Zhenyu, Li, Chong, Li, Xiao, Cao, Xun, Zhu, Hao 等 9 位作者

原文摘要

We present a unified framework for reconstructing animatable 3D human avatars from a single portrait across head, half-body, and full-body inputs. Our method tackles three bottlenecks: pose- and framing-sensitive feature representations, limited scalable data, and unreliable proxy-mesh estimation. We introduce a Dual-UV representation that maps image features to a canonical UV space via Core-UV and Shell-UV branches, eliminating pose- and framing-induced token shifts. We also build a factorized synthetic data manifold combining 2D generative diversity with geometry-consistent 3D renderings, supported by a training scheme that improves realism and identity consistency. A robust proxy-mesh tracker maintains stability under partial visibility. Together, these components enable strong in-the-wild generalization. Trained only on half-body synthetic data, our model achieves state-of-the-art head and upper-body reconstruction and competitive full-body results. Extensive experiments and analyses further validate the effectiveness of our approach.

cs.CV

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

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