Controllable, Efficient and Personalized Generative AI: From Long-Horizon Video to Interactive World Models - Robotics Institute Carnegie Mellon University

VASC Seminar

October

26
Mon
Pınar Yanardag Assistant Professor Computer Science , Virginia Tech
Monday, October 26
3:30 pm to 4:30 pm
Newell-Simon Hall 3305
Controllable, Efficient and Personalized Generative AI: From Long-Horizon Video to Interactive World Models
Abstract:  Generative models can produce compelling images and videos, yet building systems that people can reliably direct and personalize remains a fundamental challenge. These systems must support new forms of control, generate over extended horizons within practical computational budgets, and adapt to individual concepts and preferences. In this talk, I will explore these challenges through a central question: How can we make visual generation controllable, efficient, and personal?

I will present three complementary directions toward this goal. First, Infinity-RoPE (CVPR’26) revisits how autoregressive video models represent temporal context, unlocking long-horizon generation and responsive control through inference-time interventions. Next, VideoMLA (NeurIPS’26) addresses the computational demands of extended generation by learning compact representations of past context, substantially reducing memory requirements while preserving generation quality. Finally, SPAWN (NeurIPS’26) introduces personalized concepts into interactive world models, allowing users to bring their own subjects into generated environments as they navigate them.

I will close with broader directions for video generation, including new distillation methods for efficient generation, improving physical plausibility (HiPhy, NeurIPS’26), and aligning generation with desired outcomes through reinforcement learning (DPP-GRPO, CVPR’26). This research advances a broader vision for generative cinematography, supported by an NSF CAREER’26 award, toward generative worlds that remember the past, respond to our choices, and evolve as we interact with them.
Bio:  Pinar Yanardag is an Assistant Professor of Computer Science at Virginia Tech, where she leads GEMLAB (Generative Modeling Lab). Her research focuses on controllable, efficient, and personalized generative AI, with an emphasis on video generation and interactive world models. She earned her PhD from Purdue as a Fulbright Fellow and was a postdoctoral researcher at the MIT Media Lab, where her generative AI projects attracted over one million participants and media coverage from TIME, The Washington Post, and CNN. She received an NSF CAREER award for her research on generative cinematography and was recognized as an Outstanding Area Chair at CVPR 2026. Her research is supported by industry partners including Google, Adobe, Snap, and Qualcomm.
Sponsor:
The VASC seminar is generously sponsored by HeyGen, an all-in-one
AI-powered video generation platform that leverages advances in
computer vision, generative modeling, and multimodal learning to make
high-quality video creation both scalable and accessible.