CogOmniControl: Reasoning-Driven Controllable Video Generation via Creative Intent Cognition
CogOmniControl integrates CogVLM and CogOmniDiT for intent-driven video generation, outperforming existing models.
Key Findings
Methodology
CogOmniControl consists of two parts: CogVLM for reasoning creative intent from sparse and abstract conditions, trained via Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT); and CogOmniDiT, a unified video diffusion transformer that processes multimodal inputs and generates videos aligned with CogVLM outputs.
Key Results
- Result 1: On CogReasonBench, CogVLM achieved an average score of 4.47, significantly outperforming Qwen3-VL-8B-Thinking's 3.75 in creative intent cognition.
- Result 2: On CogControlBench, CogOmniControl surpassed Seedance2.0 and VINO in multimodal intent alignment and dynamic naturalness, with a 0.15 average score improvement.
- Result 3: The Best-of-N selection mechanism improved video quality and condition adherence, achieving a motion smoothness score of 0.96.
Significance
This research addresses the limitations of existing video generation models in sparse and abstract conditions, providing a more efficient solution for professional animation workflows. Its closed-loop architecture and new benchmarks set a foundation for future multimodal generation research.
Technical Contribution
Introduced the first intent-driven controllable video generation framework combining professional reasoning. CogVLM enables deep intent cognition, while CogOmniDiT translates reasoning into high-quality videos. The Best-of-N mechanism further optimizes outputs through adaptive evaluators.
Novelty
This is the first framework to integrate professional reasoning into controllable video generation, combining CogVLM and CogOmniDiT to significantly enhance performance under sparse and abstract conditions.
Limitations
- Limitation 1: High computational cost for high-resolution video generation may limit scalability.
- Limitation 2: Performance degrades under extremely complex or conflicting multimodal conditions.
- Limitation 3: Requires more diverse professional datasets for broader validation.
Future Work
Future work could focus on optimizing model efficiency to reduce computational costs, expanding datasets to cover more professional scenarios, and refining evaluator tools for better Best-of-N selection.
AI Executive Summary
Existing video generation models struggle with sparse and abstract conditions, making them unsuitable for professional animation workflows. CogOmniControl addresses this gap with a reasoning-driven framework that integrates CogVLM for intent cognition and CogOmniDiT for video generation.
Experiments demonstrate that CogOmniControl significantly outperforms open-source models on CogReasonBench and CogControlBench, particularly in multimodal intent alignment and dynamic naturalness. The Best-of-N mechanism further enhances video quality by selecting the best outputs using adaptive evaluators.
Despite its strengths, the framework's high computational cost and reliance on professional datasets limit its scalability. Future research should explore efficiency improvements and dataset expansion to advance controllable video generation technology further.
Deep Analysis
Background
Recent advances in diffusion models have revolutionized image and video generation, with models like VINO and OmniWeaving achieving high fidelity. However, these models fail to handle sparse or abstract conditions, limiting their utility in professional workflows like animation production.
Core Problem
Current video generation models lack deep understanding of creative intent when faced with complex multimodal inputs, leading to misaligned outputs. Additionally, alignment between abstract conditions and generated videos remains a challenge.
Innovation
CogOmniControl introduces: 1) CogVLM for deep intent cognition via SFT and RFT; 2) CogOmniDiT, a unified transformer for multimodal video generation; 3) a Best-of-N selection mechanism leveraging adaptive evaluators for optimal outputs.
Methodology
- �� CogVLM: Trained via SFT and RFT to enhance reasoning capabilities.
- �� CogOmniDiT: Processes multimodal inputs in a unified sequence and generates videos aligned with reasoning outputs.
- �� Best-of-N: Uses CogVLM to select evaluators for optimizing video quality.
Experiments
Experiments were conducted on CogReasonBench and CogControlBench to evaluate intent cognition and video generation quality. Benchmarks include professional animation data and general generation datasets.
Results
CogVLM achieved a 4.47 average score on CogReasonBench, outperforming baseline models. On CogControlBench, CogOmniControl scored 4.35 in multimodal intent alignment, surpassing Seedance2.0's 3.95.
Applications
The framework is directly applicable to animation production, film effects, and game development, particularly for generating high-quality videos from abstract conditions.
Limitations & Outlook
While CogOmniControl excels in benchmarks, its high computational cost and dataset dependency may limit real-world scalability. Additionally, performance under highly complex conditions requires further improvement.
Plain Language Accessible to non-experts
Imagine you're a director creating an animation. You provide a script, sketches, and reference images. CogOmniControl acts as your assistant, using CogVLM to understand your vision and CogOmniDiT to turn it into a smooth animation. It even picks the best version for you!
ELI14 Explained like you're 14
Think of designing a game character's animation. You give the game a sketch and a description like 'a girl in a blue dress dancing in a forest.' The game uses a super-smart AI to understand your idea and create the perfect animation. Cool, right?
Glossary
CogVLM
A model trained to reason creative intent from multimodal inputs.
Used to understand abstract and sparse conditions.
CogOmniDiT
A diffusion model for generating videos from multimodal conditions.
Transforms CogVLM's reasoning into high-quality videos.
SFT
Supervised fine-tuning using labeled data to enhance task performance.
Trains CogVLM for reasoning tasks.
RFT
Reinforcement fine-tuning using reward mechanisms to optimize performance.
Improves CogVLM's reasoning accuracy.
Best-of-N
A mechanism to select the best output from multiple candidates.
Optimizes video quality using adaptive evaluators.
Open Questions Unanswered questions from this research
- 1 How can computational costs for high-resolution video generation be reduced?
- 2 How can performance under extremely complex conditions be improved?
- 3 What datasets can better represent diverse professional scenarios?
Applications
Immediate Applications
Animation Production
Helps animators generate high-quality videos from sketches, reducing manual adjustments.
Film Effects
Quickly generates special effects videos aligned with directors' creative intent.
Long-term Vision
Virtual Reality
Generates dynamic content for VR environments, enhancing immersion.
Abstract
Recent diffusion models achieve strong photorealism and fluency in video generation, yet remain fragile under abstract, sparse or complex conditions, leading to poor performance in professional production workflows such as storyboard sketches and clay render conditions. Existing video generation models, either inject conditions through adapters or couple a generic vision-language model (VLM) within a diffusion backbone, leaving a capability gap and failing to produce the videos that align with the user's creative intent. We present CogOmniControl, a reasoning-driven framework that factorizes controllable video generation into creative intent cognition and generation. Specifically, we train a specialized CogVLM using authentic anime production data. Compared to generic VLMs, it generates more professional and clear outputs, accurately cognizing user creative intent from sparse and abstract conditions and tuning these cues into dense reasoning output. Besides, CogOmniDiT unifies the controls from various conditions through in-context generation and is aligned to the CogVLM reasoning outputs via reinforcement learning. Furthermore, leveraging CogVLM's robust capability in guiding video generation, we release its potential in planning specific evaluators and enable a Best-of-N selection for the generated videos. This integration transforms the entire framework into a closed-loop "harness-like" architecture. We further introduce CogReasonBench and CogControlBench, built from professional workflows data that carry genuine creative intent rather than simulated ones. Experiments on two benchmarks show that CogOmniControl surpassed the existing open-source models. The project website: https://um-lab.github.io/CogOmniControl/