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Showing 2026 · cs.CVShow all

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cs.CV2026

SyncVid: World-Equivariant Diffusion for Synchronized Video Generation

Farhad G. Zanjani, Animesh Karnewar, Fatih Porikli +1

Generating multiple videos of a dynamic scene from a single reference video requires synthesizing both observed and unobserved content consistently across viewpoints. Existing came…

cs.CV2026

How Much Can We Prune? Exploring the Performance Limits of Visual Token Pruning

Janghoon Cho, Shubhankar Borse, Reza Pourreza +4

Visual token pruning is increasingly used to reduce the memory and inference costs of vision-language models. Existing work has largely focused on designing method-specific selecto…

cs.CV2026

MagnifiQ: Patch-aware Text Guided Progressive Upscaling for High-Resolution Image Restoration

Mahesh Reddy, Yashesh Savani, Antoine Mercier +3

High-resolution image restoration from degraded inputs is challenging because it must preserve global structural consistency while recovering fine-grained local details, especially…

cs.CV2026

SNM-VFI: Symmetric Nonlinear Motion-Guided Generative Video Frame Interpolation

Jisoo Jeong, Hong Cai, Jamie Menjay Lin +5

We propose Symmetric Nonlinear Motion-guided Generative Video Frame Interpolation (SNM-VFI), a training-free framework for motion-controllable generative video frame interpolation…

cs.CV2026

MobileWan: Closing the Quality Gap for Mobile Video Diffusion

Mohsen Ghafoorian, Denis Korzhenkov, Adil Karjauv +9

Recent advances in video diffusion have been driven by scaling transformer-based architectures to billions of parameters, substantially improving visual fidelity and motion coheren…

cs.CV2026

CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers

Zhuojin Li, Hsin-Pai Cheng, Hong Cai +2

Diffusion Transformers (DiTs) deliver remarkable image and video generation quality but incur high computational cost, limiting scalability and on-device deployment. We introduce C…

cs.CV2026

Memory-Efficient Fine-Tuning Diffusion Transformers via Dynamic Patch Sampling and Block Skipping

Sunghyun Park, Jeongho Kim, Hyoungwoo Park +6

Diffusion Transformers (DiTs) have significantly enhanced text-to-image (T2I) generation quality, enabling high-quality personalized content creation. However, fine-tuning these mo…

cs.CV2026

ForeSea: AI Forensic Search with Multi-modal Queries for Video Surveillance

Hyojin Park, Yi Li, Janghoon Cho +8

Despite decades of work, surveillance still struggles in searching and reasoning about specific targets across long, multi-camera videos. Existing methods - tracking, retrieval, an…

cs.CV2026

Generative Scenario Rollouts for End-to-End Autonomous Driving

Rajeev Yasarla, Deepti Hegde, Shizhong Han +11

Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation lear…