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arXiv · 2512.13609

Do-Undo Bench: Reversibility for Action Understanding in Image Generation

Shweta Mahajan·Shreya Kadambi·Hoang Le·Rajeev Yasarla·Apratim Bhattacharyya·Munawar Hayat·Fatih Porikli
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Abstract

We introduce the Do-Undo task and benchmark to address a critical gap in vision-language models: understanding and generating plausible scene transformations driven by real-world actions. Unlike prior work that relies on prompt-based image generation and editing to perform action-conditioned image manipulation, our training hypothesis requires models to simulate the outcome of a real-world action and then reverse it to the original state. This forward-reverse requirement tests genuine cause-and-effect understanding rather than stylistic or semantic edits. We curate a high-quality benchmark of reversible actions from real-world scenarios to enable robust action grounding. Our experiments reveal that current models struggle with action reversibility, highlighting the need to evaluate action understanding. Do-Undo provides an intuitive testbed for evaluating and advancing action-aware generation in multimodal systems that must reason about real-world dynamics.

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Shweta Mahajan, Shreya Kadambi, Hoang Le, Rajeev Yasarla, Apratim Bhattacharyya, Munawar Hayat, Fatih Porikli. 2026-05-14. Do-Undo Bench: Reversibility for Action Understanding in Image Generation. https://arxiv.org/abs/2512.13609

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