Compositional Video Generation via Inference-Time Guidance

2026-05-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

Compositional Video Generation (CVG) is a novel inference-time guidance method designed to enhance compositional faithfulness in frozen text-to-video diffusion models. The technique addresses common failures in generating videos that require fine-grained understanding of relations between entities, attributes, actions, and motion directions, without necessitating generator retraining. CVG operates by steering the denoising process using the model's internal grounding signals, specifically leveraging cross-attention maps that encode how prompt concepts are grounded across space and time. A lightweight compositional classifier is trained on these attention features, and its gradients are applied during early denoising steps to guide the latent trajectory towards the desired composition. This approach improves compositional generation without modifying the model architecture, fine-tuning the generator, or requiring external controls like layouts or bounding boxes, while preserving the visual quality of the underlying generator.

Key takeaway

For research scientists developing or deploying text-to-video models, CVG offers a method to significantly improve compositional accuracy without the computational cost of fine-tuning or architectural changes. You should consider integrating inference-time guidance techniques, particularly those leveraging internal attention mechanisms, to enhance prompt faithfulness in complex video generation tasks, thereby extending the utility of existing frozen models.

Key insights

Inference-time guidance using internal grounding signals improves compositional faithfulness in frozen text-to-video models.

Principles

• Cross-attention maps encode spatial and temporal concept grounding.
• Lightweight classifiers can steer denoising with attention features.

Method

Train a lightweight compositional classifier on cross-attention features. Use its gradients during early denoising steps to steer the latent trajectory toward desired compositions in frozen text-to-video models.

In practice

• Improve video generation without retraining models.
• Enhance compositional accuracy in text-to-video outputs.

Topics

• Compositional Video Generation
• Inference-Time Guidance
• Text-to-Video Diffusion Models
• Cross-Attention Maps
• Denoising Process

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

Related on AIssential

• VPG: Visual Prefix Guidance for Autoregressive Image and Video Generation — VPG is a training-free inference method that improves autoregressive generation by strengthening prefix posterior support.
• Benchmarking Single-Factor Physical Video-to-Audio Generation — Generative V2A models struggle with physical reasoning, often prioritizing text cues over visual data for sound generation.
• Consistency-Preserving Diverse Video Generation — A new flow-matching framework improves video diversity and temporal consistency using latent-space gradient regulation.
• Building Generative Image & Video models at Scale - Sander Dieleman (Veo and Nano Banana) — Diffusion models generate high-quality audiovisual data through iterative denoising of learned compressed latent representations.
• LCG: Long-Context Consistent Image Generation with Sparse Relational Attention — LCG uses sparse attention and consistency constraints for scalable, coherent multi-image generation.
• Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning — The generation pathway of visual thoughts encodes reasoning that can be distilled for efficient, text-only multimodal models.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.