arXiv:2606.28593v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have achieved significant improvements on static visual-to-code tasks such as generating code for webpages, charts, or SVGs, it remains unclear whether they can recover temporal dynamics when motion is present. To this end, we introduce Animation2Code, a benchmark for evaluating temporal visual reasoning via reconstructing executable web animation code from videos. Animation2Code consists of 1,069 web animation videos with diverse visual appearances and motion patterns, paired with corresponding HTML/C
The proliferation of advanced vision-language models necessitates benchmarks that test temporal reasoning, which is a significant new frontier for VLM capabilities.
This development pushes the boundaries of VLM application by specifically addressing video-to-code generation, enabling more dynamic and complex AI-driven content creation and automation.
VLMs are now being systematically evaluated on their ability to understand and reproduce temporal dynamics from video, moving beyond static image analysis for code generation.
- · AI researchers
- · Creative industries
- · Web developers
- · VLM developers
- · Manual web animation coders
Improved VLMs will more effectively translate motion in video into executable code, enhancing automation in digital content creation.
This capability could lead to new AI tools for generating interactive experiences or automating aspects of game development based on visual input.
The mastery of temporal video-to-code could facilitate AI agents designing and deploying complex, reactive digital environments autonomously.
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Read at arXiv cs.AI