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20232026
most citedTopology-aware Federated Learning in Edge Computing: A Comprehensive Survey

112 citations · 304 across the 149 of their papers we have counts for

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cs.CV2026

Masked Visual Actions for Unified World Modeling

Hadi Alzayer, Wenlong Huang, Haonan Chen +8

Video models absorb rich priors over how the visual world moves, interacts, and responds to contact, making them promising substrates for robotic world modeling. The central challe…

cs.CV2026

APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts

Emily Jin, Joy Hsu, Yiqing Xu +3

Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility. To successfully execute a task, a robot must decompose goals, select…

cs.CV2026

HarmVideoBench: Benchmarking Harmful Video Understanding in Large Multimodal Models

Jiajun Wu, Haoyu Kang, Yining Sun +13

Large vision-language models (LVLMs) have recently shown immense potential in automated content moderation, sparking growing interest in developing harmful-video benchmarks. Howeve…

cs.CV2026

VPA-Guard: Defending and Benchmarking Image-to-Video Generation Against Visual Prompt Attacks

Yining Sun, Haoyu Kang, Jiajun Wu +7

Recent advancements in Image-to-Video (I2V) generation have transformed input images from simple appearance references into interactive control interfaces where visual cues such as…

cs.CV2026

Linear Scaling Video VLMs for Long Video Understanding

Cristobal Eyzaguirre, Jiajun Wu, Juan Carlos Niebles

Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention, causing compu…

cs.CV2026

NeuROK: Generative 4D Neural Object Kinematics

Chen Geng, Guangzhao He, Yue Gao +3

Data-driven approaches have revolutionized 3D vision, enabling transformers to effectively reconstruct and generate static 3D objects. However, generating simulative 4D dynamics --…

cs.CV2026

GPIC: A Giant Permissive Image Corpus for Visual Generation

Keshigeyan Chandrasegaran, Kyle Sargent, Suchir Agarwal +6

Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image Corpus of approximately 28 tri…

cs.CV2026

ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop

Yining Hong, Jiageng Liu, Han Yin +5

Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a function of action. Rather than passi…

cs.CV2026

Fishbone: From One 3D Asset to a Million Controllable Edits

Yumeng He, Xiaoying Wang, Peihao Li +7

Large-scale controllable 3D assets are critical for computer graphics, embodied AI, robotics, and interactive content creation, yet creating diverse 3D assets remains challenging d…

cs.CV2026

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model

Qiguang Chen, Chengyu Luan, Jiajun Wu +7

Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks f…

cs.CV2026

AnyLift: Scaling Motion Reconstruction from Internet Videos via 2D Diffusion

Hongjie Li, Heng Yu, Jiaman Li +4

Reconstructing 3D human motion and human-object interactions (HOI) from Internet videos is a fundamental step toward building large-scale datasets of human behavior. Existing metho…

cs.CV2026

Self-Consistency for LLM-Based Motion Trajectory Generation and Verification

Jiaju Ma, R. Kenny Jones, Jiajun Wu +1

Self-consistency has proven to be an effective technique for improving LLM performance on natural language reasoning tasks in a lightweight, unsupervised manner. In this work, we s…

cs.CV2026

GenFusion: Feed-forward Human Performance Capture via Progressive Canonical Space Updates

Youngjoong Kwon, Yao He, Heejung Choi +4

We present a feed-forward human performance capture method that renders novel views of a performer from a monocular RGB stream. A key challenge in this setting is the lack of suffi…

cs.CV2026

Physion-Eval: Evaluating Physical Realism in Generated Video via Human Reasoning

Qin Zhang, Peiyu Jing, Hong-Xing Yu +7

Video generation models are increasingly used as world simulators for storytelling, simulation, and embodied AI. As these models advance, a key question arises: do generated videos…

cs.CV2026

Thinking with Spatial Code for Physical-World Video Reasoning

Jieneng Chen, Wenxin Ma, Ruisheng Yuan +3

We introduce Thinking with Spatial Code, a framework that transforms RGB video into explicit, temporally coherent 3D representations for physical-world visual question answering. W…

cs.CV2026

RealWonder: Real-Time Physical Action-Conditioned Video Generation

Wei Liu, Ziyu Chen, Zizhang Li +3

Current video generation models cannot simulate physical consequences of 3D actions like forces and robotic manipulations, as they lack structural understanding of how actions affe…

cs.CV2026

SurfPhase: 3D Interfacial Dynamics in Two-Phase Flows from Sparse Videos

Yue Gao, Hong-Xing Yu, Sanghyeon Chang +5

Interfacial dynamics in two-phase flows govern momentum, heat, and mass transfer, yet remain difficult to measure experimentally. Classical techniques face intrinsic limitations ne…

cs.CV2026

SAW-Bench: Learning Situated Awareness in the Real World

Chuhan Li, Rilyn Han, Joy Hsu +5

A core aspect of human perception is situated awareness, the ability to relate ourselves to the surrounding physical environment and reason over possible actions in context. Howeve…

cs.CV2026

PerpetualWonder: Long-Horizon Action-Conditioned 4D Scene Generation

Jiahao Zhan, Zizhang Li, Hong-Xing Yu +1

We introduce PerpetualWonder, a hybrid generative simulator that enables long-horizon, action-conditioned 4D scene generation from a single image. Current works fail at this task b…

cs.CV2026

Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes

Jing Tan, Zhaoyang Zhang, Yantao Shen +6

We introduce Talk2Move, a reinforcement learning (RL) based diffusion framework for text-instructed spatial transformation of objects within scenes. Spatially manipulating objects…

cs.CV2026

Choreographing a World of Dynamic Objects

Yanzhe Lyu, Chen Geng, Karthik Dharmarajan +4

Dynamic objects in our physical 4D (3D + time) world are constantly evolving, deforming, and interacting with other objects, leading to diverse 4D scene dynamics. In this paper, we…

cs.CV2025

Autoregressive Flow Matching for Motion Prediction

Johnathan Xie, Stefan Stojanov, Cristobal Eyzaguirre +2

Motion prediction has been studied in different contexts with models trained on narrow distributions and applied to downstream tasks in human motion prediction and robotics. Simult…

cs.CV2025

VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression

Kyle Sargent, Ruiqi Gao, Philipp Henzler +5

Evaluations of image compression performance which include human preferences have generally found that naive distortion functions such as MSE are insufficiently aligned to human pe…

cs.CV2025

WonderZoom: Multi-Scale 3D World Generation

Jin Cao, Hong-Xing Yu, Jiajun Wu

We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited…