Seen-to-Scene: Keep the Seen, Generate the Unseen for Video Outpainting

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

Seen-to-Scene is a new framework for video outpainting that expands video content beyond original frame boundaries while maintaining spatial fidelity and temporal coherence. Current methods, often based on large-scale generative models like diffusion models, struggle with implicit temporal modeling and limited spatial context, leading to inconsistencies in dynamic scenes and large outpainting tasks. Seen-to-Scene addresses these issues by unifying propagation-based and generation-based paradigms. It utilizes flow-based propagation with a flow completion network, initially pre-trained for video inpainting and then fine-tuned end-to-end to ensure coherent motion fields. The framework also incorporates reference-guided latent propagation to efficiently and reliably propagate source content across frames, demonstrating superior temporal coherence and visual realism compared to prior methods.

Key takeaway

For research scientists developing video generation or editing tools, Seen-to-Scene offers a robust approach to overcome temporal inconsistencies in video outpainting. You should consider integrating hybrid propagation-generation paradigms and fine-tuning pre-trained inpainting networks to achieve superior temporal coherence and visual realism in your models, especially for dynamic scenes or large expansions. This method reduces the need for input-specific adaptation, streamlining development.

Key insights

Seen-to-Scene unifies propagation and generation for video outpainting, improving temporal coherence and realism.

Principles

• Unify propagation and generation.
• Fine-tune pre-trained networks.
• Use reference-guided propagation.

Method

Seen-to-Scene uses a flow completion network, pre-trained for video inpainting and fine-tuned end-to-end, combined with reference-guided latent propagation to reconstruct coherent motion fields for video outpainting.

In practice

• Apply flow-based propagation.
• Integrate pre-trained inpainting models.
• Employ reference-guided content propagation.

Topics

• Video Outpainting
• Generative Models
• Flow-based Propagation
• Temporal Coherence
• Latent Propagation

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

Related on AIssential

• Mode Seeking meets Mean Seeking for Fast Long Video Generation — Decoupling local fidelity and long-term coherence enables minute-scale video generation from limited long-form data.
• See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation — Render-feedback-aware visual planning significantly improves LLM-generated educational animation quality by addressing spatial and temporal defects.
• Can We Build Scene Graphs, Not Classify Them? FlowSG: Progressive Image-Conditioned Scene Graph Generation with Flow Matching — FlowSG progressively generates scene graphs by jointly denoising discrete semantics and continuous geometry via hybrid flow matching.
• Controllable Video Object Insertion via Multiview Priors — Multi-view priors and dual-path conditioning enable consistent, realistic video object insertion.
• Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment — Direct 3D scene generation via unified mesh-texture modeling and semantic alignment overcomes 2D representation limitations.
• Learning Long-Term Motion Embeddings for Efficient Kinematics Generation — Efficient long-term motion generation is achieved by learning highly compressed motion embeddings and using conditional flow-matching.

Open in AIssential →

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