Looking Alive

An interactive, perceptive Leonardo da Vinci.

A character that doesn't just talk at you. It notices you, and has the restraint to know when not to.

…strong model implementations of the deep neural network and GMM-HMM, with clear optimizations.

The build, in five chapters

One character across four bodies, plus the idea that ties them together. Two chapters have work you can read today: The Mind is built and running, and The Face is underway. The last two are mapped. Open any chapter below.

The Body

The character, fully embodied.

Everything converges: the perception-and-decision engine, the expressive face, and proven sim-to-real motion, running on a physical robot that shares a real space with guests. A character that notices the specific person in front of it, then turns, leans, and plays to them with a body.

Someone forgets, for a second, that it's a machine.

A physical platform built for expressive, safe motion

On-board, real-time perception and decision-making

Whole-of-character motion: gaze, posture, and gesture together

Live interaction with guests in a public space

The PhD arc is built to bring four researchers' strengths together: a believable, deployable character that perceives and reasons about people, explainably, then steps off the screen into a physical, reactive robot.

Markus Gross

Interactive digital characters and the technology that makes them feel present, including projection into physical space.

Joseph Campbell

Theory of mind, anticipating human intent, and interpretable interaction, the backbone of the “explain every decision” principle.

Heni Ben Amor

Reactive control and robot learning: characters and robots that respond to people in the moment, the engine behind the robotic phase.

Stelian Coros

Physics-based, expressive character and robot motion, how a believable performance transfers to a body that obeys physics.

The single most repeatable bit of theme-park magic is a character who makes a guest feel seen. Today that depends on a gifted human performer. This builds it as a real-time, repeatable, explainable system: a character that notices the specific guest in front of it, reacts in persona, plays to a crowd, and eventually steps off the screen into the room. Da Vinci is the first host; the perception and decision engine is the product.

[1]Wampfler, R., et al. (2025). A Platform for Interactive AI Character Experiences (Digital Einstein). SIGGRAPH Conf. Papers '25.
[2]Campbell, J. & Ben Amor, H. (2017). Bayesian Interaction Primitives: A SLAM Approach to Human-Robot Interaction. CoRL, PMLR 78.
[3]Campbell, J., Stepputtis, S. & Ben Amor, H. (2019). Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks. RSS. arXiv:1908.04955.
[4]Oguntola, I., Campbell, J., Stepputtis, S. & Sycara, K. (2023). Theory of Mind as Intrinsic Motivation for Multi-Agent RL. ICML Workshop. arXiv:2307.01158.
[5]Zhang, X.-J., et al. (2025). Model-Agnostic Policy Explanations with Large Language Models. COLM. arXiv:2504.05625.
[6]Serifi, A., et al. (2024). Robot Motion Diffusion Model (RobotMDM): Motion Generation for Robotic Characters. SIGGRAPH Asia.
[7]Coros, S., et al. (2013). Computational Design of Mechanical Characters. ACM TOG 32(4), SIGGRAPH.
[8]Bates, J. (1994). The Role of Emotion in Believable Agents. Communications of the ACM 37(7).

[9]Cheng, T., Song, L., Ge, Y., et al. (2024). YOLO-World: Real-Time Open-Vocabulary Object Detection. CVPR. arXiv:2401.17270.
[10]Jocher, G., et al. (2024). Ultralytics YOLO11 (software).
[11]Zhang, Y., Sun, P., Jiang, Y., et al. (2022). ByteTrack: Multi-Object Tracking by Associating Every Detection Box. ECCV. arXiv:2110.06864.
[12]Abdelrahman, A. A., et al. (2022). L2CS-Net: Fine-Grained Gaze Estimation.
[13]Lin, T.-Y., Maire, M., Belongie, S., et al. (2014). Microsoft COCO: Common Objects in Context. ECCV.
[14]Glas, D. F., Shiomi, M., Kanda, T., et al. (2017). Personal Greetings: Personalizing Robot Utterances Based on Novelty of Observed Behavior. Int. J. of Social Robotics.