Jeet Vora

I’m a researcher working at the intersection of Computer Vision, Video Understanding, Multi-Camera Visual Perception, and Spatio-Temporal Learning, with growing research interests in 3D Vision, Dynamic 4D Scene Understanding, and World Models. My work combines fundamental research with large-scale real-world AI systems, spanning applications in creative media and sports analytics.

I’m currently working as a Research Consultant with the IVUL Lab at King Abdullah University of Science and Technology (KAUST), Prof. Bernard Ghanem’s group; primarily collaborating with Dr. Silvio Giancola, Dr. Merey Ramazanova, and Jintao Ma on large-scale video understanding, multimodal perception, and spatio-temporal learning for sports analytics.

Previously, I worked as a Research Engineer at Animaker, where I led AI research and development for large-scale video creation platforms. My work involved designing and deploying production-grade AI systems across products like Steve.ai, Animaker, Picmaker and Vmaker, spanning problems such as Generative Video AI, Script-to-Video Generation, Talking-Head Animation, Video Matting, and Multimodal Retrieval.

Before this, I completed my Master’s by Research in Computer Science at IIIT Hyderabad. I was advised by Dr. Vineet Gandhi and was associated with the CVIT Lab. My research focused on Multi-Camera Detection and Tracking, specifically focusing on robust Generalization in Deep Multi-View Pedestrian Detection across unseen camera configurations and environments. As part of this work, I explored simulation-to-real (Sim2Real) transfer by synthetically generating datasets using the GTA-V and Unity game engines.

Alongside my research and engineering work, I’m passionate about mentorship and teaching. I’ve mentored students and professionals in end-to-end AI/ML projects through collaborations with TalentSprint and InLustro Learning. I’ve also delivered hands-on sessions for institutional clients including L&T Madh Training Academy and MLR Institute of Technology.

More broadly, I’m interested in advancing machine perception through dynamic scene understanding, visual reasoning, and structured representations of dynamic environments. My long-term goal is to develop intelligent systems capable of understanding, reconstructing, and reasoning about the visual world while bridging fundamental research and impactful real-world applications.