A Hands-On Evaluation of Multimodal Model Code Generation: From Web Animation to 3D Video

1. Background: Multimodal Models Are Changing Front-End Development Workflows

In the past, building an interactive web animation as a front-end developer typically meant handling several stages in sequence: structural design, styling, animation logic, interaction state management, and cross-browser compatibility testing. As multimodal foundation models continue to evolve, more development scenarios are starting to delegate the loop of “requirement description—code generation—effect verification—iterative refinement” to model-assisted workflows.

The core value of multimodal models is that they are no longer limited to plain text input. They can simultaneously understand images, code, text, and even video information, then perform reasoning and generation on top of that understanding. This capability is particularly well suited to the “what you see is what you get” iteration pattern common in front-end development: a developer can provide a reference image or a natural language requirement, and the model directly returns runnable front-end code. If the result is not satisfactory, the developer can continue requesting corrections through conversation.

This article uses a concrete exercise—generating a web animation of an electric fan with adjustable speed settings, and then generating a 3D rotating video from a static image—to observe and analyze how new-generation multimodal models actually perform in code generation, visual understanding, and video synthesis.

2. Test Design: A Seemingly Simple Web Animation Task Full of Details

To evaluate model capability objectively, this test chose a front-end task that appears straightforward but contains a large number of details: generate a web animation of an electric fan with adjustable speed settings and a clean, tech-styled bottom UI.

The task examines the following key capabilities:

1. Visual reproduction: The model needs to understand the typical visual characteristics of an electric fan, including blade shape, protective grille structure, and base proportions.
2. Animation implementation: The blade rotation animation must account for the rotation axis, speed control, and smooth transitions when switching speed levels.
3. Interaction design: The speed control UI should have good usability and visual consistency.
4. Code quality: The generated HTML/CSS/JavaScript should be clearly structured, maintainable, and directly runnable in a browser.
5. First-attempt success rate: Ideally, the model should produce a satisfactory result on the first generation, reducing the cost of manual correction.

3. Practice Process and Observations

3.1 First Round: Performance of a Conventional Reasoning Model

The first test used a conventional reasoning model. After receiving the prompt, the model did generate a complete block of HTML code. The code included basic structures such as the fan head, blades, and base, and implemented a basic rotation animation.

However, when the code actually ran in a browser, problems quickly emerged:

• Distorted blade shape: The generated blades were recognizable in a static view, but once they started rotating, the overall effect differed significantly from a real electric fan because the blade shape, rotation axis, and rotation trajectory were not designed realistically enough. It looked more like a rotating geometric figure.
• Rough visual details: Details such as the fan grille and base connector were missing. The overall impression was closer to a schematic diagram than a usable visual component.
• Unsatisfactory first generation: The first version of the code was almost unusable as-is and had to be refined through subsequent iterations.

After identifying the problems, the developer gave the model explicit correction requests, such as “the blades don’t look like a real fan” and “the rotation effect is unrealistic.” In the second generation, the model adjusted the blade shape and rotation logic, and the result improved somewhat. However, it was still far from the goal of “usable after one generation.”

3.2 Second Round: Performance of a New-Generation Multimodal Reasoning Model

The same prompt was then used with a new-generation multimodal reasoning model. The result showed that the model delivered a significantly more complete version on its first attempt:

• Blade shape closer to reality: The model generated blades with more plausible curvature and angles. The visual trajectory during rotation matched the motion of real fan blades much more closely.
• Grille and structural details: A grille-like structure was added to the fan head. Although still a simplified visual representation, the overall appearance was much more realistic.
• Clear animation logic: The blade rotation around the central axis was implemented correctly, and the relationship between rotation speed and speed level was clearly defined.
• UI design met the requirements: The bottom control area had a clean, tech-inspired visual style, and the interactive states of the speed buttons were immediately clear.

Notably, this result was achieved on the first generation, without any manual correction iterations. This reflects significant improvements in the new-generation model in the following areas:

• Richer visual prior knowledge: The model’s visual understanding of an electric fan is closer to human cognition, enabling it to generate code that better matches real physical forms.
• Tighter coupling between code generation and visual output: The model does not just generate code that “runs”—it generates code that “looks right.” This suggests it has better internal prediction of the final rendered result during generation.

3.3 Advanced Test: From Static Image to 3D Animated Video

After completing the web animation generation, another capability was tested: generating a 3D animated video of a rotating fan from an existing image.

The test used the better static fan image generated earlier as input and asked the model to produce a short video that conveys a 3D rotation effect.

The results showed that the new-generation multimodal model could complete the video generation task quickly, producing a video of approximately 7 seconds. The frame rate before conversion was 60 frames per second. In terms of visual quality, the fan rotation in the video was generally smooth, and the spatial depth of the viewing angle was reasonably represented. Although there were still some noticeable flaws in the details—for example, the lighting changes during rotation were not entirely natural—the overall completion quality was impressive for an automated result generated from a static image.

This capability has clear practical value for developers who need to quickly produce product demos, proof-of-concept videos, or social media content.

4. Technical Analysis: Why Does the New-Generation Model Perform Better?

Based on the test results, the advantages of the new-generation multimodal reasoning model on complex front-end generation tasks can be attributed to several technical factors:

4.1 Enhanced Visual-Code Joint Reasoning

Traditional code generation models rely primarily on textual context for reasoning and have limited “imagination” of the final rendered result. New-generation multimodal models incorporate more visual modality information during training, allowing them to perform a kind of internal “simulation preview” of the rendered output while generating code. This joint reasoning capability directly improves the visual accuracy of generated code.

4.2 Deeper Understanding of Physical World Common Sense

The question of “whether a rotating electric fan looks like a real fan” is essentially a test of physical common sense. The visual behavior of rotating blades involves multiple implicit physical factors, including centrifugal force direction, blade pitch, and the sense of airflow. The new-generation model models this kind of common sense more fully, resulting in more realistic animation output.

4.3 Improved Organization of Long Context and Complex Structures

A complete electric fan web animation involves HTML structure, CSS styling, JavaScript animation logic, and responsive design considerations. The model needs to maintain structural consistency and maintainability across a long context. In this test, the new-generation model generated well-organized code, demonstrating stronger handling of long-range dependencies.

4.4 Extension of Multi-Task Coordination

Moving from “generating web code” to “generating a 3D animated video” appears to be two separate tests, but together they reflect the model’s overall capability in cross-modal transformation tasks. The new-generation model can more naturally establish mappings between text, code, images, and video, making it useful in more complex application scenarios.

5. Practical Implications for Developers

Based on the observations from this test, the following suggestions may be useful for developers in their daily work:

5.1 Incorporate Multimodal Models into Front-End Prototyping

For scenarios that require rapid visual validation, multimodal models can serve as prototyping tools that significantly shorten the time from idea to interactive prototype. When tasks involve visual reproduction and animation, the first-generation quality of new models is already at a level of practical usability.

5.2 Pay Attention to Prompt Design and Refinement

Even with significantly improved model capabilities, prompt quality remains a critical factor in generation results. In this test, the supplementary description “the bottom UI should be cleaner with a tech feel” effectively guided the model toward the expected control interface. Clear style descriptions and functional requirements help the model focus on key objectives.

5.3 Allocate Manual Correction Effort Wisely

Although new-generation models have greatly improved first-attempt success rates, manual intervention may still be needed for complex interaction logic or highly customized visual styles. Development teams should establish a closed-loop mechanism of “model generation—automated verification—manual review—targeted correction,” directing human effort toward tasks that genuinely require judgment and creativity.

5.4 Watch for New Cross-Modal Generation Use Cases

The ability to generate 3D animated videos from static images opens up new possibilities for product showcases, concept demos, and content creation. Developers can actively explore how this capability fits into their own business scenarios, such as quickly converting design mockups into dynamic prototypes or automatically generating matching visual assets for structured content.

6. Limitations and Outlook

Despite the strong performance of the new-generation multimodal reasoning model in this test, its limitations should be viewed objectively:

• Room for improvement in detail realism: The generated video still falls short of professional 3D rendering in subtle dimensions such as lighting and texture.
• Code maintainability needs further validation: Although the code structure is good, integrating model-generated code with an existing codebase in large projects still requires human oversight.
• Controllability of generation results: The model’s output style may vary under different input conditions. Maintaining stability and consistency in production environments remains a challenge for engineering implementation.

Looking ahead, as multimodal models continue to improve in visual understanding, physical simulation, and code generation, their application in automated front-end development, interactive prototyping, and multimedia content generation is likely to become deeper and more widespread. For developers on the front lines, closely tracking these advances and integrating useful capabilities into existing workflows will help find a new balance between efficiency and quality.