activity
20182026
most citedIn the Eye of the Beholder: Gaze and Actions in First Person Video

8 citations · 34 across the 59 of their papers we have counts for

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53 papers · 1 filter

cs.CV2026

LeRF: Learning Reference Coordinate Frames for Perspective Taking Reasoning

Bang Xiao, Wenqi Jia, Ozgur Kara +5

Perspective taking is a fundamental component of spatial intelligence, requiring models interpret spatial relations from a specified viewpoint, such as that of another entity or an…

cs.CV2026

Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation

Yutong Liu, Nan Huang, Xu Cao +1

Recent advancements in unified generative models (UGMs) and world simulators have achieved unprecedented results in visual perception and synthesis. However, these models primarily…

cs.CV2026

Kirin: Animal Motion Generation from In-the-Wild Video

Brian Nlong Zhao, Zhuoyang Pan, James M. Rehg +2

Understanding animal motion is fundamental to modeling animal behavior and biomechanics, yet progress in this area lags far behind human motion research due to the scarcity of high…

cs.CV2026

A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities

Ozgur Kara, Tarik Can Ozden, Furkan Horoz +6

Diffusion models have become the dominant family of generative models in the visual domain. However, their widespread public availability enables misuse at scale, motivating a rapi…

cs.CV2026

Decoding Children's Gait Behavior

Yifan Shen, Boyi Li, Meihuan Huang +12

We introduce a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video. We specifically target the ambulator…

cs.CV2026

CogniRoute: Learning to Route Social Evidence in Omni-Modal Models

Yifan Shen, Pei Tian, Xinzhuo Li +8

Omni-modal models can ingest video, audio, and text, but unified access to multiple modalities does not guarantee that a model uses the right evidence. This gap is especially prono…

cs.CV2026

ST-DiffEye: Diffusion-based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling

Brian Nlong Zhao, Ozgur Kara, Junho Kim +1

We study the problem of human gaze modeling, which aims to generate the gaze patterns a viewer produces while observing a visual stimulus. Gaze is primarily captured through two mo…

cs.CV2026

When Eyes Betray AI: Social Gaze Consistency as a Semantic Cue for AI-Generated Image Detection

Jihyeon Kim, Sohee Kim, Soosan Lee +3

Recent generative models have largely closed the gap on low-level artifacts - pixel fingerprints, frequency anomalies, upsampling traces - particularly in person-centric and partia…

cs.CV2026

GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions

Junho Kim, Xu Cao, Houze Yang +6

Understanding social interactions requires reasoning over subtle non-verbal cues, yet current multimodal large language models (MLLMs) often fail to identify who interacts with who…

cs.CV2026

IAM: Identity-Aware Human Motion and Shape Joint Generation

Wenqi Jia, Zekun Li, Abhay Mittal +6

Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches…

cs.CV2026

Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation

Zeqian Long, Ozgur Kara, Haotian Xue +2

Image-to-video (I2V) generation has the potential for societal harm because it enables the unauthorized animation of static images to create realistic deepfakes. While existing def…

cs.CV2026

Self-Improving 4D Perception via Self-Distillation

Nan Huang, Pengcheng Yu, Weijia Zeng +4

Large-scale multi-view reconstruction models have made remarkable progress, but most existing approaches still rely on fully supervised training with ground-truth 3D/4D annotations…

cs.CV2026

STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding

Junho Kim, Hosu Lee, James M. Rehg +2

Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require…

cs.CV2026

EgoForge: Goal-Directed Egocentric World Simulator

Yifan Shen, Jiateng Liu, Xinzhuo Li +9

Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint changes, frequent hand-object intera…

cs.CV2026

Vinedresser3D: Agentic Text-guided 3D Editing

Yankuan Chi, Xiang Li, Zixuan Huang +1

Text-guided 3D editing aims to modify existing 3D assets using natural-language instructions. Current methods struggle to jointly understand complex prompts, automatically localize…

cs.CV2026

Toward Cognitive Supersensing in Multimodal Large Language Model

Boyi Li, Yifan Shen, Yuanzhe Liu +12

Multimodal Large Language Models (MLLMs) have achieved remarkable success in open-vocabulary perceptual tasks, yet their ability to solve complex cognitive problems remains limited…

cs.CV2025

How Much 3D Do Video Foundation Models Encode?

Zixuan Huang, Xiang Li, Zhaoyang Lv +1

Videos are continuous 2D projections of 3D worlds. After training on large video data, will global 3D understanding naturally emerge? We study this by quantifying the 3D understand…

cs.CV2025

Toward Diffusible High-Dimensional Latent Spaces: A Frequency Perspective

Bolin Lai, Xudong Wang, Saketh Rambhatla +4

Latent diffusion has become the default paradigm for visual generation, yet we observe a persistent reconstruction-generation trade-off as latent dimensionality increases: higher-c…

cs.CV2025

Cue3D: Quantifying the Role of Image Cues in Single-Image 3D Generation

Xiang Li, Zirui Wang, Zixuan Huang +1

Humans and traditional computer vision methods rely on a diverse set of monocular cues to infer 3D structure from a single image, such as shading, texture, silhouette, etc. While r…

cs.CV2025

Layer-Aware Video Composition via Split-then-Merge

Ozgur Kara, Yujia Chen, Ming-Hsuan Yang +3

We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods…

cs.CV2025

DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images

Ozgur Kara, Harris Nisar, James M. Rehg

Numerous models have been developed for scanpath and saliency prediction, which are typically trained on scanpaths, which model eye movement as a sequence of discrete fixation poin…

cs.CV2025

Improving Personalized Search with Regularized Low-Rank Parameter Updates

Fiona Ryan, Josef Sivic, Fabian Caba Heilbron +3

Personalized vision-language retrieval seeks to recognize new concepts (e.g. "my dog Fido") from only a few examples. This task is challenging because it requires not only learning…

cs.CV2025

Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs

Yifan Shen, Yuanzhe Liu, Jingyuan Zhu +6

Current Vision-Language Models (VLMs) struggle with fine-grained spatial reasoning, particularly when multi-step logic and precise spatial alignment are required. In this work, we…

cs.CV2025

ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models

Ozgur Kara, Krishna Kumar Singh, Feng Liu +3

Current diffusion-based text-to-video methods are limited to producing short video clips of a single shot and lack the capability to generate multi-shot videos with discrete transi…