Adil Karjauv

Hello, welcome to my personal page!

I am currently a Machine Learning Researcher (Senior Engineer) at Qualcomm AI Research working on efficient video generative AI. My work significantly contributed to the following:

• MobileWan: Led an architecture optimization effort for the largest video generation diffusion transformer running on-device (based on Wan2.2-5B), developed a novel structured pruning method, improving efficiency without sacrificing quality. Paper under review.
• PyramidalWan: Co-developed a step distillation method for a pyramidally-distilled video generation model, achieving a 10x inference speedup. Published at CVPR 2026; patent filed.
• Neodragon: Contributed to step distillation techniques (2.5-10x speedup) for the first diffusion transformer based mobile video generation model. Presented as a NeurIPS 2025 demo and published at ICLR 2026; patent to be filed.
• MoViE: Developed and optimized a state-of-the-art diffusion model for video editing, achieving an 87x on-device speedup – the fastest diffusion-based video editing demo shown on-device. Presented at NeurIPS 2024 and CVPR 2025 as a demo.
• Object-Centric Diffusion (OCD): Contributed to training-free techniques that reallocate compute toward salient regions in video editing, cutting latency up to 10x with comparable quality. Published at ECCV 2024.
• Pioneered the first on-device deep-learning-based video denoising solution at high resolution (QHD/4K at 30FPS); patent filed.

Prior to that, I completed a Master’s degree at KAIST in Robotics and Computer Vision (RCV) Lab under supervision of Prof. In So Kweon. My primary focus was on adversarial machine learning and its applications in multimedia. This work led to several publications in top-tier conferences and workshops such as CVPR, ICCV, NeurIPS, ICLR, and others.

I am always open to new opportunities and collaborations, so please feel free to reach out!