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From the 1 of 11 linked papers with an AI index.

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11 papers

cs.CV2026

KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation

Yuqi Tang, Tengfei Liu, Yizheng Lai +18

The paper introduces KeyFrame-Compass, a benchmark and evaluation framework for assessing how well video generation models follow supplied keyframes while preserving overall video…

cs.CV2026

CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales

Xinlong Chen, Jiafu Tang, Yue Ding +12

Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can ob…

cs.CV2026

LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV

Tengfei Liu, Yang Shi, Xuanyu Zhu +17

Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to short-form settings. Existing b…

cs.CL2026

OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models

Yue Ding, Yiyan Ji, Jungang Li +12

Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences le…

cs.AI2026

RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark

Yang Shi, Yuhao Dong, Yue Ding +22

The integration of visual understanding and generation into unified multimodal models represents a significant stride toward general-purpose AI. However, a fundamental question rem…

cs.CV2026

Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers

Bozhou Li, Yushuo Guan, Haolin Li +7

Recent DiT-based text-to-image models increasingly adopt LLMs as text encoders, yet text conditioning remains largely static and often utilizes only a single LLM layer, despite pro…