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20242026
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cs.CV2026

Motion Attribution for Video Generation

Xindi Wu, Despoina Paschalidou, Jun Gao +5

Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a m…

cs.CV2026

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

Bingyu Li, Da Zhang, Tao Huo +3

Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains…

cs.CV2026

Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players

Fangfu Liu, Kai He, Tianchang Shen +7

World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single control signal. However, many gen…

cs.CV2026

MoRight: Motion Control Done Right

Shaowei Liu, Xuanchi Ren, Tianchang Shen +5

Generating motion-controlled videos--where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints--demands two capabilities: (1) disentangl…

cs.CV2026

IntroSVG: Learning from Rendering Feedback for Text-to-SVG Generation via an Introspective Generator-Critic Framework

Feiyu Wang, Jiayuan Yang, Zhiyuan Zhao +4

Scalable Vector Graphics (SVG) are central to digital design due to their inherent scalability and editability. Despite significant advancements in content generation enabled by Vi…

cs.CV2025

ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation

Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen +11

Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, wh…