Designing an AI-based solution allowing user's to virtually try-on blazers.
GANs
Encoder-decoder network
Google Cloud
Elizabeth Venorsky
Users need a way to virtually try on blazers through a blazer boutique’s mobile or web application, so that they can efficiently and confidently purchase a blazer, reducing the rate of returns and exchanges. Incorporating principles of user experience (UX) design, the AI would display the selected blazer on the user, adjusting for correct color and dimensions, and conforming to the user's shape using their laptop or phone camera. Allowing users to try on blazers virtually would save them time and money, enabling them to stay home rather than visiting the store. Additionally, it would enhance user experience by allowing interaction and engagement with the product compared to traditional browsing. Lastly, it would personalize the shopping experience by showing the user a blazer that fits their exact body shape, size, and preference. This feature would also benefit the boutique by reducing returns or exchanges caused by sizing issues. Virtual try-on would increase conversion rates by reducing user uncertainty and instilling confidence in their purchases.
Data Gathering Efforts:
Incorporating UX design principles, data gathering efforts would involve curating a diverse dataset of blazers by sourcing images or 3D models from various brands and retailers. A combination of datasets for training, such as Dress Code or DeepFashion, would be utilized, ensuring representation for various user preferences and body types. These datasets contain high-resolution images of the upper body for both male and female models. Additionally, Generative Adversarial Networks (GANs) would be employed to create virtual try-on images, with the discriminator network assessing the realism of the generated images. An encoder-decoder network would extract features from input images, including the person’s body shape. Dataset annotation, labeling clothing regions in images, would facilitate segmentation tasks, ensuring accurate virtual try-on experiences.
Infrastructure:
UX design would influence the infrastructure design, ensuring a seamless and intuitive user experience. The infrastructure would utilize cloud services, specifically Google Cloud, to ensure scalability and resource accessibility. GPUs would be utilized for training the GAN and encoder-decoder networks to achieve efficient model training, optimizing processing speed for users. Camera integration would rely on APIs for camera functionality within the boutique’s mobile and web app, enabling users to capture images effortlessly. The dataset and trained models would be stored on Google Cloud Storage for scalability and accessibility, with a user-friendly interface for easy access. GitLab would be incorporated for managing changes and facilitating collaboration within the team, promoting efficient development cycles and continuous improvement based on user feedback.
ETL (Extract, Transform, Load) Stage:
UX design principles would guide the ETL stage, focusing on simplifying processes and enhancing user interaction. The ETL stage would commence with data preprocessing, including image resizing, pixel value adjustment, and dataset augmentation to enhance variability and robustness. Feature engineering would extract clothing attributes and key points from the dataset to aid in model training and improve performance, ensuring accurate virtual try-on experiences. Real-time ETL pipelines would handle incoming user-generated content efficiently, minimizing wait times for users and providing instant feedback on virtual try-on results.
Data Cleaning:
In the data cleaning stage, UX design would emphasize user-centric quality checks and validation processes. Quality checks would be conducted to ensure the accuracy and consistency of annotations, with manual validations as necessary to maintain high standards of virtual try-on accuracy. Additionally, statistical analysis and anomaly detection would be employed to identify and address any outliers in the dataset, preventing them from affecting model training and ensuring reliable virtual try-on experiences for users.
In the second step, we will focus on recording and gathering, moving and storing, exploring and transforming, analysis, testing, and synthesizing and optimizing the AI. Incorporating principles of user experience (UX) design, we will prioritize user-centric approaches to ensure a seamless and intuitive experience throughout the process.
Starting with recording and gathering, we will use a diverse training dataset that includes Dress Code and Deep Fashion, considering various user preferences and body types. The datasets encompass a range of blazer styles, colors, sizes, and textures, catering to diverse user needs. Additionally, we will allow for user-generated content, empowering users to contribute their own images and videos, thereby enhancing the dataset and fostering user engagement. To improve spatial context and segmentation accuracy, we will leverage depth sensors alongside camera sensors on users' computers or phones, ensuring precise virtual try-on experiences.
In the moving and storing phase, we will prioritize reliable data flows, infrastructure, pipelines, and ETL processes with a focus on enhancing user experience. Real-time data streaming will be implemented to handle data from users' cameras, providing seamless and responsive interactions. Event-driven processing techniques will ensure scalability and responsiveness, optimizing the user experience. Our infrastructure, primarily relying on the Google Cloud platform, will be designed for accessibility and efficiency, enhancing user accessibility and usability. Additionally, serverless computing will be integrated for event-driven workloads, further optimizing performance and responsiveness. The design of an ETL pipeline will prioritize simplicity and efficiency, ensuring smooth data extraction and processing.
In the explore and transform phase, UX design principles will guide cleaning and quality checks to ensure accuracy and consistency. Manual validations will be conducted to maintain high standards of data quality, promoting trust and reliability in the virtual try-on experience. Identifying and addressing outliers in the dataset will be crucial for enhancing model training and ensuring accurate virtual try-on results, aligning with user expectations and preferences.
In the analyze and label phase, we will focus on analyzing user interactions, performance metrics, and feedback to iterate and improve the design. Performance metrics, such as interaction response time and rendering quality, will be assessed from a user-centric perspective, prioritizing smooth and realistic virtual try-on experiences. User feedback will be actively solicited and analyzed to identify pain points and areas for improvement, informing iterative design iterations aimed at optimizing user satisfaction and engagement.
In the learn and test phase, we will employ A/B testing of different variants, ensuring user preferences and needs are considered in the evaluation process. Simple ML algorithms and clustering techniques will be applied with a focus on predicting user engagement and delivering personalized recommendations, aligning with user preferences and behavior patterns.
In the Synthesize, train, and optimize phase, user research will drive the selection and optimization of AI models, prioritizing realism and accuracy in virtual try-on experiences for our users. GANs will be utilized to generate realistic images of users wearing blazers, catering to diverse user preferences and style choices. The Encoder-Decoder Network will be leveraged for precise image segmentation, ensuring accurate fitting and alignment of virtual blazers to the user's body.
Throughout the process, user feedback and performance metrics will be continuously monitored and analyzed to iteratively improve the virtual try-on experience. Additionally, privacy and security measures will be integrated into the software development plan to safeguard user data and ensure compliance with regulations, promoting trust and confidence in the system.
There are several challenges we may encounter when building and implementing the AI. The first challenge lies in ensuring the accuracy and realism of the try-on experience for users. Achieving precise fitting and realistic rendering of blazers could pose a significant challenge. To address this, we will continuously research and enhance the GAN architecture and encoder-decoder networks to improve the accuracy of the virtual try-on experience for users.
Another challenge we may face is biased recommendation, a form of AI bias. Biased recommendations could occur within the try-on system, as the system may favor certain styles, colors, brands, and sizes over others, leading to skewed representations. This bias may stem from insufficient diversity in the dataset and the algorithms used to develop the try-on system.
To mitigate the effects of anticipated biased recommendations, we will adopt strategies to ensure a more diverse training dataset for the try-on system. This will involve incorporating a wide range of diverse trends, sizes, colors, styles, and demographic preferences. Additionally, we will implement monitoring mechanisms to assess recommendations and feedback, identifying any patterns of bias within the AI system. Furthermore, conducting user testing will be essential to gather insights, feedback, and responses on the recommendations from the try-on system, allowing us to understand any biases or patterns noticed from a user’s perspective. Synthesizing this data and reviewing trends will guide our efforts to address biases effectively.
Privacy and security present another challenge, as establishing safeguards and building user trust in the try-on system's security is crucial. Users may have concerns regarding the security of their data, especially regarding its collection. To address this, we will explore techniques such as differential privacy to protect user privacy while still enabling model training on distributed user data.
Ongoing research will be vital to the success of the blazer virtual-try on experience. Proactively seeking to enhance and optimize the AI model will lead to a better experience for users. We will continue researching techniques for accurate body pose estimations to better align the size and fit of the blazer on the user. Exploring methods for simulating the texture and movement of virtual fabrics will contribute to creating a realistic virtual try-on experience. Lastly, we will continue testing and iterating based on user research to ensure continuous improvement of the user experience.
