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

arXiv:2609.22069 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/discus0434/aestheti

Project page: https://wxliii.github.io/OmniVBench/7

Terminology

• Fidelity — 71× in this paper · explained in 2 episodes
• Benchmark — 28× in this paper · explained in 2 episodes
• Reference Fidelity — 22× in this paper
• Instruction Realization — 18× in this paper
• Consistency — 16× in this paper · explained in 3 episodes
• Core — 15× in this paper · explained in 2 episodes
• Video Quality — 11× in this paper
• Grounding — 5× in this paper · explained in 3 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 3× in this paper
• Evaluation Dimensions — 3× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Cooperation — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In International Conference on Computer Vision — 1× in this paper
• Motion Primitives — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models
• VINO: A Unified Visual Generator with Interleaved OmniModal Context
• Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset
• Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
• DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation
• LTX-Video: Realtime Video Latent Diffusion
• HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation
• Kling-Omni Technical Report
• Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding
• Bernini: Latent Semantic Planning for Video Diffusion
• OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning
• Seedance 2.0: Advancing Video Generation for World Complexity
• Wan: Open and Advanced Large-Scale Video Generative Models
• HarmoView: Harmonizing Multi-View Constraints for Identity-Consistent Video Generation
• Unified Reward Model for Multimodal Understanding and Generation
• HunyuanVideo 1.5 Technical Report
• Qwen-Image Technical Report
• LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
• MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation

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