Author name cluster

Pablo Pueyo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

AAAI Conference 2026 Conference Paper

CineMPC: A Fully Autonomous Drone Cinematography System Incorporating Zoom, Focus, Pose, and Scene Composition (Abstract Reprint)

Pablo Pueyo
Juan Dendarieta
Eduardo Montijano
Ana Cristina Murillo
Mac Schwager

We present CineMPC, a complete cinematographic system that autonomously controls a drone to film multiple targets recording user-specified aesthetic objectives. Existing solutions in autonomous cinematography control only the camera extrinsics, namely, its position and orientation. In contrast, CineMPC is the first solution that includes the camera intrinsic parameters in the control loop, which are essential tools for controlling cinematographic effects such as focus, zoom, and depth of field. The system is validated in real-world experiments.

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IROS Conference 2024 Conference Paper

CLIPSwarm: Generating Drone Shows from Text Prompts with Vision-Language Models

Pablo Pueyo
Eduardo Montijano
Ana C. Murillo
Mac Schwager

This paper introduces CLIPSwarm, a new algorithm designed to automate the modeling of swarm drone formations based on natural language. The algorithm begins by enriching a provided word, to compose a text prompt that serves as input to an iterative approach to find the formation that best matches the provided word. The algorithm iteratively refines formations of robots to align with the textual description, employing different steps for "exploration" and "exploitation". Our framework is currently evaluated on simple formation targets, limited to contour shapes. A formation is visually represented through alpha-shape contours and the most representative color is automatically found for the input word. To measure the similarity between the description and the visual representation of the formation, we use CLIP [1], encoding text and images into vectors and assessing their similarity. Sub-sequently, the algorithm rearranges the formation to visually represent the word more effectively, within the given constraints of available drones. Control actions are then assigned to the drones, ensuring robotic behavior and collision-free movement. Experimental results demonstrate the system’s efficacy in accurately modeling robot formations from natural language descriptions. The algorithm’s versatility is showcased through the execution of drone shows in photorealistic simulation with varying shapes. We refer the reader to the supplementary video for a visual reference of the results.

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IROS Conference 2023 Conference Paper

CineTransfer: Controlling a Robot to Imitate Cinematographic Style from a Single Example

Pablo Pueyo
Eduardo Montijano
Ana C. Murillo
Mac Schwager

This work presents CineTransfer, an algorithmic framework that drives a robot to record a video sequence that mimics the cinematographic style of an input video. We propose features that abstract the aesthetic style of the input video, so the robot can transfer this style to a scene with visual details that are significantly different from the input video. The framework builds upon CineMPC, a tool that allows users to control cinematographic features, like subjects' position on the image and the depth of field, by manipulating the intrinsics and extrinsics of a cinematographic camera. However, CineMPC requires a human expert to specify the desired style of the shot (composition, camera motion, zoom, focus, etc). CineTransfer bridges this gap, aiming a fully autonomous cinematographic platform. The user chooses a single input video as a style guide. CineTransfer extracts and optimizes two important style features, the composition of the subject in the image and the scene depth of field, and provides instructions for CineMPC to control the robot to record an output sequence that matches these features as closely as possible. In contrast with other style transfer methods, our approach is a lightweight and portable framework which does not require deep network training or extensive datasets. Experiments with real and simulated videos demonstrate the system's ability to analyze and transfer style between recordings, and are available in the supplementary video 1 1 https://youtu.be/_QzNz5WUtpk

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ICRA Conference 2022 Conference Paper

CineMPC: Controlling Camera Intrinsics and Extrinsics for Autonomous Cinematography

Pablo Pueyo
Eduardo Montijano
Ana C. Murillo
Mac Schwager

We present CineMPC, an algorithm to autonomously control a UAV-borne video camera in a nonlinear Model Predicted Control (MPC) loop. CineMPC controls both the position and orientation of the camera-the camera extrinsics-as well as the lens focal length, focal distance, and aperture-the camera intrinsics. While some existing solutions autonomously control the position and orientation of the camera, no existing solutions also control the intrinsic parameters, which are essential tools for rich cinematographic expression. The intrinsic parameters control the parts of the scene that are focused or blurred, the viewers' perception of depth in the scene and the position of the targets in the image. CineMPC closes the loop from camera images to UAV trajectory and lens parameters in order to follow the desired relative trajectory and image composition as the targets move through the scene. Experiments using a photo-realistic environment demon-strate the capabilities of the proposed control framework to successfully achieve a full array of cinematographic effects not possible without full camera control.

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IROS Conference 2020 Conference Paper

CinemAirSim: A Camera-Realistic Robotics Simulator for Cinematographic Purposes

Pablo Pueyo
Eric Cristofalo
Eduardo Montijano
Mac Schwager

Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular in the film and entertainment industries, in part because of their maneuverability and perspectives they enable. While there exists methods for controlling the position and orientation of the drones for visibility, other artistic elements of the filming process, such as focal blur, remain unexplored in the robotics community. The lack of cinematographic robotics solutions is partly due to the cost associated with the cameras and devices used in the filming industry, but also because state-of-the-art photo-realistic robotics simulators only utilize a full in-focus pinhole camera model which does not incorporate these desired artistic attributes. To overcome this, the main contribution of this work is to endow the well-known drone simulator, AirSim, with a cinematic camera as well as extend its API to control all of its parameters in real time, including various filming lenses and common cinematographic properties. In this paper, we detail the implementation of our AirSim modification, CinemAirSim, present examples that illustrate the potential of the new tool, and highlight the new research opportunities that the use of cinematic cameras can bring to research in robotics and control.

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