Abstract: This seminar will focus on how vectorized representations can be generated from raster signals to enhance spatial intelligence. I will discuss the core methodology behind this transformation, with a focus on applications in AR/VR and robotics. The seminar will also briefly cover follow-up work that explores rigging and re-animating objects from casual single videos without templates, showcasing the potential of this approach in scaling 3D content creation.

Bio: Mosam Dabhi is pursuing PhD at Carnegie Mellon University, specializing in computer vision and AI, with a focus on transforming raster signals into vectorized representations for spatial intelligence. His work has contributed to enhancing the performance of production headsets and XR devices, improving their spatial responsiveness. Mosam has developed scalable algorithms for collecting 3D ground truth data, which are critical for real-world applications. His key contributions include the development of the 3D Lifting Foundation Model (3D LFM) and RAT4D, enabling 3D content generation from casual videos. His research has applications in robotics, AR/VR, and spatial intelligence, advancing AI’s capability to interact with the physical world.

Homepage:  https://mosamdabhi.github.io

Sponsored in part by:   Meta Reality Labs Pittsburgh