What Comes Next? Omni-StoryBench for Evaluating Story-Grounded Omnimodal Generation

arXiv:2609.37317 · cs.CV, cs.MM · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.MM

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/AIDASLab/Omni-Storybench

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 54× in this paper · explained in 2 episodes
• Consistency — 40× in this paper · explained in 3 episodes
• Aggregation — 26× in this paper · explained in 1 episode
• Alignment — 26× in this paper · explained in 1 episode
• Accuracy — 19× in this paper · explained in 1 episode
• LLM judge — 9× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 8× in this paper · explained in 3 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Commentary — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Flow Matching — 3× in this paper · explained in 7 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• In International Conference on Learning Representations — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Qwen3-VL Technical Report
• Llama-Nemotron: Efficient Reasoning Models
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• EXAONE 4.5 Technical Report
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
• Audio Flamingo Next: Next-Generation Open Audio-Language Models for Speech, Sound, and Music
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
• Dynin-Omni: Omnimodal Unified Large Diffusion Language Model
• Kimi-Audio Technical Report
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion
• UniM: A Unified Any-to-Any Interleaved Multimodal Benchmark
• NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching
• HyperCLOVA X 8B Omni
• Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
• GPT-4o System Card
• Movie Gen: A Cast of Media Foundation Models
• Learning Transferable Visual Models From Natural Language Supervision

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models