What did you personally accomplish this week on the project?
From last week’s progress, I have noticed that using Dynamic Fusion to iteratively find the most compact 3D reconstruction of user is probably not the most efficient approach to solve the problem.
Since we are specifically concerned with the human face, rather than reconstructing a full volumetric model dynamically, we can leverage pre-defined facial priors and structured depth-based 3D face models for a more efficient solution.
To refine this approach, I explored alternative methods for 3D face modeling and rendering that use depth maps combined with facial priors rather than relying solely on iterative fusion. Additionally, I investigated texture blending techniques that adjust AR overlays based on lighting conditions, ensuring realistic makeup application from different angles.
Ultimately, this would allow me to simplify my 3D reconstruction process and target the output specifically to provide necessary information for texture blending with a pre-defined makeup texture map.
Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
So far, I have found some paper that was able to map 3D input from the Kinect depth sensor to the Candide-3 model, which I am looking to implement with the Realsense camera and test the refined depth-based 3D face model.
I have started the implementation but not yet finished so I am a little bit behind schedule as I also had to work on Design Presentation this week.
For the texture blending section, that will be the focus for the next task after coming back from Spring Break – AR Overlay Rendering Prototype section.
What deliverables do you hope to complete in the next week?
Finalize the 3D face modeling framework using depth-based priors for improved performance. Hopefully I should be able to get a 3D reconstruction model of my own face once I receive all the parts I need (still waiting for the Realsense cable to arrive)