Show Me: Unifying Instructional Image and Video Generation with Diffusion Models

TL;DR

ShowMe introduces a unified diffusion-based framework for instructional image and video generation, improving consistency and realism by combining spatial and temporal modeling with novel rewards.

Contribution

The paper presents a novel unified diffusion model that handles both image manipulation and video prediction, integrating spatial and temporal components with consistency rewards.

Findings

Outperforms expert models in instructional image generation

Achieves superior results in video prediction benchmarks

Enhances structural fidelity and temporal coherence

Abstract

Generating visual instructions in a given context is essential for developing interactive world simulators. While prior works address this problem through either text-guided image manipulation or video prediction, these tasks are typically treated in isolation. This separation reveals a fundamental issue: image manipulation methods overlook how actions unfold over time, while video prediction models often ignore the intended outcomes. To this end, we propose ShowMe, a unified framework that enables both tasks by selectively activating the spatial and temporal components of video diffusion models. In addition, we introduce structure and motion consistency rewards to improve structural fidelity and temporal coherence. Notably, this unification brings dual benefits: the spatial knowledge gained through video pretraining enhances contextual consistency and realism in non-rigid image edits, while the instruction-guided manipulation stage equips the model with stronger goal-oriented reasoning for video prediction. Experiments on diverse benchmarks demonstrate that our method outperforms expert models in both instructional image and video generation, highlighting the strength of video diffusion models as a unified action-object state transformer.