Creating new videos using head pose transfer from existing clips

A video correspondence platform partnered with us to implement generative AI technology capable of mapping celebrity facial features and body movements. By synthesizing deep learning with classical computer vision, we transformed a complex R&D concept into a production-ready reality. 

Our customer in the digital solutions space sought to use generative AI technology to map facial features and body movements from short snippets of celebrity talk shows. The goal was to create a more realistic and immersive visual experience for their users. However, operationalizing this vision surfaced several complex technical hurdles: 

• Data Quality: Capturing high-quality source footage that could authentically mimic complex human expressions and movements. 

• Research Constraints: Generating poses with expressions presents a challenge, and this aspect is currently in the research phase 

• Expression Transfer: Accurately transferring a full spectrum of human expressions onto a target identity is still constrained by current technology. 

To address these limitations, we developed a hybrid approach to balance innovation with reliability: 

• We combined deep learning models with classical computer vision algorithms to establish a more stable foundation for movement mapping. 

• To improve the visual output, we implemented a dedicated method for refining poses and utilized advanced technology specifically for transferring facial expressions.  

• To ensure the final result met the platform’s quality standards, we applied upscaling and restoration techniques to the final video output, enhancing the clarity and realism of the generated correspondence. 

Outcomes 

• Generated outputs closely resembling real video footage, improving visual authenticity. 

• Moved the solution to production. Currently, it is in the alpha-100 release stage