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OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation

Paper recorded by Signals 4 on 2026-09-18 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.CV · 计算机视觉 · first seen 2026-09-21

Abstract

Reference-to-video (R2V) generation is evolving toward increasingly general and versatile reference control, giving rise to the emerging paradigm of omni R2V generation. However, existing benchmarks fall short of these emerging capabilities: their test cases cover limited reference types and compositions, and their evaluation protocols largely assess holistic reference consistency, overlooking whe

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#17 most recent of 270 cs.CV papers we have recorded · ↑ newer: MintAct: A Unified Visual Agent for Digital Environments · ↓ older: Traffic Sign Recognition for Autonomous Driving Using Branched YOLOv2
Cite this page: OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation: the #17 most recent of 270 cs.CV papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/omnivbench-a-benchmark-and-large-scale-dataset-for-omni-reference-to-video-gener.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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