2025 | Student

MUSE Design Awards Silver Winner

Dadan! – AI-Powered Mockups for Product Photography

Entrant Company

Shih Chien University

Category

Product Design - UX / UI / IxD

Client's Name

Personal Project

Country / Region

Taiwan

Dadan! is an AI-powered tool designed to streamline the proposal process for product photography in creative agencies and photography studios. By enabling proposal teams to generate product photography mockups instantly, it eliminates the prolonged creative alignment phase and shortens project timelines.

This solution addresses a common challenge faced by both agencies and brands. Typically, after discussing creative direction, proposal teams must prepare mockups to align expectations, often relying on internal design teams. This dependency extends project timelines, yet mockups remain essential—relying solely on reference images or hand-drawn sketches results in 20% of shoots running over time or requiring reshoots, impacting efficiency and client satisfaction.

Dadan! offers significant value:
1. For teams without in-house mockup capabilities, it enhances creative communication, improving shoot satisfaction and strengthening client relationships.
2. For teams already using mockups, it accelerates project workflows, allowing for more client engagements or freeing up design teams to focus on high-value tasks like retouching and graphic design.

Dadan! was developed based on insights from in-person interviews with 10 creative agencies, brands, and photography studios, analyzing their proposal formats and workflow pain points. The tool deconstructs the three essential elements of product photography—composition, background, and lighting—into an intuitive interface, allowing proposal teams to adjust and control these elements in real-time. This ensures rapid validation of creative concepts and precise visual alignment with clients.

By minimizing misalignment risks and reducing inefficiencies, Dadan! revolutionizes the product photography workflow, making creative collaboration faster, more accurate, and more efficient.

Credits

Shih Chien University
Chunwei Lin
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