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新论文 · v1arXiv:2611.06896

超越0.55的在线边加权匹配

Online Edge-Weighted Matching Beyond 0.55

Alireza Farhadi, Mohammad Hajiaghayi

AI 速读 基于摘要

作者提出一个0.556195竞争比的随机算法，用于带自由处置的边加权在线二分匹配，改进了Blanc和Charikar的0.5368保证。

cs.DS · cs.GT

arXiv ↗PDF ↗
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We give a $0.556195$-competitive randomized algorithm for edge-weighted online bipartite matching with free disposal, improving the $0.5368$ guarantee of Blanc and Charikar. The result holds for arbitrary nonnegative edge weights and any input sequence fixed independently of the algorithm's randomness. Our algorithm builds on online correlated selection, introduced by Fahrbach, Huang, Tao, and Zadimoghaddam, and its multiway extension due to Blanc and Charikar. Our main contribution is a state-dependent primal--dual analysis of correlated selection. At each weight threshold, we track the state of the selection process jointly with an auxiliary failure event. The analysis accounts for arrivals below the threshold, which change the state without improving the matching value at that threshold. A potential function bounds the dual increase in both cases. We instantiate the analysis with an explicit win map that assigns a common score to an initial range of states. Its selection inequalities are verified by a finite computer-assisted proof covering every required candidate multiplicity and every real threshold; the verifiers are available in an accompanying repository. Triangular unit-weight inputs asymptotically attain the guarantee of the underlying ideal algorithm, proving that its analysis is tight.

首次观测 · 记录 #1CVF · CVPR 2026:cvf:260aed6678c72fb64beaa6e0d72a7d73CVF · CVPR 2026

Learning to Control Physically-simulated 3D Characters via Generating and Mimicking 2D Motions

Li, Jianan, Chen, Xiao, Huang, Tao, Wong, Tien-Tsin

原文摘要

Video data is more cost-effective than motion capture data for learning 3D character controllers, yet using it to generate realistic and physically plausible motions remains challenging. Previous approaches typically rely on off-the-shelf motion reconstruction techniques to extract 3D kinematic trajectories for physics-based imitation. However, these approaches are fundamentally limited by the physical implausibility of the estimated motions and lack a unified framework for handling non-human subjects and complex human-object interactions (HOI). We tackle this challenge by introducing Mimic2DM, a novel motion imitation framework that learns the control policy directly and solely from 2D motions extracted from videos. By formulating a 2D motion tracking objective, we train a general single-view tracking policy capable of imitating arbitrary reference motions in a physics simulation using exclusively 2D data. Furthermore, by training on 2D motions from diverse viewpoints, our view-agnostic policy implicitly learns 3D motion structures, allowing it to perform full 3D motion tracking simply by aggregating multiple views. To extend this capability, we integrate the tracking policy with an autoregressive transformer-based 2D motion generator, constructing a hierarchical control framework capable of long-horizon motion synthesis and interactive control. We show that the proposed approach is versatile and effectively synthesizes physically plausible and diverse motions across a range of domains, including dancing, soccer dribbling, and animal locomotion, without any reliance on explicit 3D motion data.

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:2a75940ac91223a728c6b98cc6affafcCVF · CVPR 2026

Do You Have Freestyle? Expressive Humanoid Locomotion via Audio Control

Li, Zhe, Chi, Cheng, Wei, Yangyang, Zhu, Boan, Huang, Tao, Sun, Zhenguo, Peng, Yibo, Wang, Pengwei 等 12 位作者

原文摘要

Humans intuitively move to sound, but current humanoid robots lack expressive improvisational capabilities, confined to predefined motions or sparse commands. Generating motion from audio and then retargeting it to robots relies on explicit motion reconstruction, leading to cascaded errors, high latency, and disjointed acoustic-actuation mapping. We propose RoboPerform, the first unified audio-to-locomotion framework that can directly generate music-driven dance and speech-driven co-speech gestures from audio. Guided by the core principle of "motion = content + style", the framework treats audio as implicit style signals and eliminates the need for explicit motion reconstruction. RoboPerform integrates a ResMoE teacher policy for adapting to diverse motion patterns and a diffusion-based student policy for audio style injection. This retargeting-free design ensures low latency and high fidelity. Experimental validation shows that RoboPerform achieves promising results in physical plausibility and audio alignment, successfully transforming robots into responsive freestyle performers capable of reacting to audio.

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:9662f6ee206eaa9d4cddfedef8389cf6CVF · CVPR 2026

IAFMNet: Information-Aware Feature Modulation for Efficient Super-Resolution

Xu, Junwei, Liu, Mengzu, Wang, Zhenyu, Wu, Fangfang, Wu, Sijia, Huang, Tao, Dong, Weisheng

原文摘要

Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input, a task that becomes increasingly challenging under real-world computational constraints. However, most efficient SISR methods adopt lightweight, spatially uniform strategies that allocate equal computation and attention across all regions, ignoring the uneven distribution of visual complexity. From an information-theoretic perspective, textures and edges inherently carry more critical information, resulting in reconstruction errors that are disproportionately concentrated in these regions. This motivates the allocation of greater computational resources and attention to these informative areas. In this paper, we propose IAFMNet, an Information-Aware Feature Modulation network for efficient SR. At its core lies the Information Density Map (IDM), which is estimated in an unsupervised manner by minimizing the Information Entropy Loss, thereby highlighting informative regions with high estimated encoding costs. Guided by the IDM, IAFMNet adopts a synergistic dual-branch design: (1) a sparse convolution branch that dynamically allocates computation to informative areas while bypassing low-information regions, and (2) an implicit modulation branch that adaptively emphasizes complex regions through information-aware affine transformations. Extensive experiments demonstrate that IAFMNet effectively identifies informative regions and achieves superior visual fidelity with reduced computational overhead.

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:a24241605736f82ffe4f0e293f32ea6bCVF · CVPR 2026

Affordance Field Intervention: Enabling VLAs to Escape Memory Traps in Robotic Manipulation

Xu, Siyu, Wang, Zijian, Wang, Yunke, Xia, Chenghao, Huang, Tao, Xu, Chang

原文摘要

Vision-Language-Action (VLA) models have shown great performance in robotic manipulation by mapping visual observations and language instructions directly to actions. However, they remain brittle under distribution shifts: when test scenarios change, VLAs often reproduce memorized trajectories instead of adapting to the updated scene, which is a failure mode we refer to as the "Memory Trap". This limitation stems from the end-to-end design, which lacks explicit 3D spatial reasoning and prevents reliable identification of actionable regions in unfamiliar environments. To compensate for this missing spatial understanding, 3D Spatial Affordance Fields (SAFs) can provide a geometric representation that highlights where interactions are physically feasible, offering explicit cues about regions the robot should approach or avoid. We therefore introduce Affordance Field Intervention (AFI), a lightweight hybrid framework that uses SAFs as an on-demand plug-in to guide VLA behavior. Our system detects memory traps through proprioception, repositions the robot to recent high-affordance regions, and proposes affordance-driven waypoints that anchor VLA-generated actions. A SAF-based scorer then selects trajectories with the highest cumulative affordance. Extensive experiments demonstrate that our method achieves an average improvement of 23.5% across different VLA backbones (pi-0 and pi-0.5) under out-of-distribution scenarios on real-world robotic platforms, and 20.2% on the LIBERO-Pro benchmark, validating its effectiveness in enhancing VLA robustness to distribution shifts. Project page: https://vla-afi.github.io/.

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 ↗