Carl Vondrick

YM Associate Professor of Computer Science
Columbia University

Office: 618 CEPSR
Address: 530 West 120th St, New York, NY 10027
Email: vondrick at cs dot columbia dot edu

Brief Bio

I am a professor in the computer science department at Columbia, and my research is broadly interested in computer vision, machine learning, and their applications.

Previously, I was a research scientist on the machine perception team at Google, and I was a visiting researcher at Cruise. I completed my PhD at MIT in 2017 advised by Antonio Torralba and my BS at UC Irvine in 2011, where I got my start working with Deva Ramanan.


By training machines to observe and interact with their surroundings, our research aims to create robust and versatile models for perception. Our lab often investigates visual models that capitalize on large amounts of unlabeled data and transfer across tasks and modalities. Other interests include scene dynamics, sound and language and beyond, interpretable models, and perception for robotics.

Our lab recruits one or two PhD students each fall. Prospective PhD students should apply to the PhD program.

PhD Students and Postdocs

Arjun Mani
NSF PhD Fellow
Basile Van Hoorick
PhD Student
Ege Ozguroglu
PhD Student
Mia Chiquier
Amazon PhD Fellow
Purva Tendulkar
Apple PhD Fellow
Presidential Fellow
Ruoshi Liu
PhD Student
Sachit Menon
NSF PhD Fellow
Presidential Fellow
Sreehari Rammohan
PhD Student
Sruthi Sudhakar
NSF PhD Fellow
Utkarsh Mall
Postdoctoral Fellow
Former PhD Students
Didac Suris, now Research Scientist at Meta
PhD Thesis: Multimodal Representations for Video (2024)
Microsoft PhD Fellowship
Chengzhi Mao, now Assistant Professor at Rutgers
PhD Thesis: Robust Machine Learning by Integrating Context (2023)
Former MS/BS Students

Sumit Sarin (2024), Ishaan Preetam Chandratreya (2023), Revant Teotia (2023), Scott Geng, (2023), Hui Lu, (2022), Jillian Ross (2021), Amogh Gupta (2020), Dave Epstein (2020)


Representative Papers

Our research creates perception systems with diverse skills, including spatial, physical, logical, and reasoning abilities, for flexibly analyzing visual data. Our multimodal approach provides versatile representations for tasks like 3D reconstruction, visual question answering, and recognition, while offering inherent explainability and excellent zero-shot generalization. The below papers highlight key examples of these capabilities.

We create interpretable machine learning methods for perception that allow people to audit decisions and reprogram representations. Unlike black-box neural networks, we develop methods that are explainable by construction while still offering excellent performance.

Machine perception is challenging because most knowledge about our world, such as physical commonsense, is not written down. Through large amounts of unlabeled video and interaction with the natural world, we create algorithms that learn perceptual skills without manual supervision.

Our research creates visual representations that learn the human behavior continuum, capturing the goals underlying human action. We aim to create computer vision systems that can assist people at their activities, thereby enabling new opportunities for human-computer interaction.

Critical applications require systems that are trustworthy and reliable. Our research demonstrates that predictive models have intrinsic empirical and theoretical advantages for improving robustness and generalization.

We develop multi-modal learning methods for robotics, integrating vision, sound, interaction, and other modalities together in order to learn representations for perception, design, and action.

We create new representations for spatial awareness, allowing vision systems to reconstruct scenes in 3D and anticipate object dynamics in the future. We often tightly integrate geometry, physics, and generative models in order to equip 3D vision systems with intuitive, and sometimes un-intuitive, physical skills.

We harness language to learn neuro-symbolic methods for computer vision, establishing methods that rapidly generalize to open world tasks while offering inherent explainability too.

Central to our research is forming an integrative perspective on perception to build accurate and robust models. Our research exploits the natural synchronization between vision, sound, and other modalities to learn cross-modal representations for tasks like recognition, source localization, and artistic correspondence.

The ubiquity of machine perception creates exciting possibilities for applications, but simultaneously exposes significant potential risks. Our research is exploring methods that prevent computer vision methods from solving potentially harmful problems.


How Video Meetings Change Your Expression
Sumit Sarin, Utkarsh Mall, Purva Tendulkar, Carl Vondrick
arXiv 2024
Paper Project Page

Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
Basile Van Hoorick, Rundi Wu, Ege Ozguroglu, Kyle Sargent, Ruoshi Liu, Pavel Tokmakov, Achal Dave, Changxi Zheng, Carl Vondrick
arXiv 2024
Paper Project Page

Evolving Interpretable Visual Classifiers with Large Language Models
Mia Chiquier, Utkarsh Mall, Carl Vondrick
arXiv 2024
Paper Project Page

SelfIE: Self-Interpretation of Large Language Model Embeddings
Haozhe Chen, Carl Vondrick, Chengzhi Mao
ICML 2024
Paper Project Page

PaperBot: Learning to Design Real-World Tools Using Paper
Ruoshi Liu, Junbang Liang, Sruthi Sudhakar, Huy Ha, Cheng Chi, Shuran Song, Carl Vondrick
arXiv 2024
Paper Project Page

pix2gestalt: Amodal Segmentation by Synthesizing Wholes
Ege Ozguroglu, Ruoshi Liu, Dídac Surís, Dian Chen, Achal Dave, Pavel Tokmakov, Carl Vondrick
CVPR 2024
Paper Project Page

Raidar: geneRative AI Detection viA Rewriting
Chengzhi Mao, Carl Vondrick, Hao Wang, Junfeng Yang
ICLR 2024

Interpreting and Controlling Vision Foundation Models via Text Explanations
Haozhe Chen, Junfeng Yang, Carl Vondrick, Chengzhi Mao
ICLR 2024

Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
Rundi Wu, Ruoshi Liu, Carl Vondrick, Changxi Zheng
ICLR 2024
Paper Project Page

Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Utkarsh Mall, Cheng Perng Phoo, Meilin Liu, Carl Vondrick, Bharath Hariharan, Kavita Bala
ICLR 2024


Objaverse-XL: A Universe of 10M+ 3D Objects
Matt Deitke, et al.
NeurIPS 2023

ViperGPT: Visual Inference via Python Execution for Reasoning
Dídac Surís*, Sachit Menon*, Carl Vondrick
ICCV 2023 (Oral)
Paper Project Page Code

Zero-1-to-3: Zero-shot One Image to 3D Object
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, Carl Vondrick
ICCV 2023
Paper Project Page Code Demo

Muscles in Action
Mia Chiquier, Carl Vondrick
ICCV 2023
Paper Project Page

SurfsUp: Learning Fluid Simulation for Novel Surfaces
Arjun Mani*, Ishaan Preetam Chandratreya*, Elliot Creager, Carl Vondrick, Richard Zemel
ICCV 2023
Paper Project Page

Landscape Learning for Neural Network Inversion
Ruoshi Liu, Chengzhi Mao, Purva Tendulkar, Hao Wang, Carl Vondrick
ICCV 2023
Paper Blog Post

SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors
Hongge Chen, Zhao Chen, Greg Meyer, Dennis Park, Carl Vondrick, Ashish Shrivastava, and Yuning Chai
ICCV 2023

Robust Perception through Equivariance
Chengzhi Mao, Lingyu Zhang, Abhishek Joshi, Junfeng Yang, Hao Wang, Carl Vondrick
ICML 2023
Paper Project Page

What You Can Reconstruct from a Shadow
Ruoshi Liu, Sachit Menon, Chengzhi Mao, Dennis Park, Simon Stent, Carl Vondrick
CVPR 2023
Paper Blog Post

Tracking through Containers and Occluders in the Wild
Basile Van Hoorick, Pavel Tokmakov, Simon Stent, Jie Li, Carl Vondrick
CVPR 2023
Paper Project Page Datasets Code

FLEX: Full-Body Grasping Without Full-Body Grasps
Purva Tendulkar, Dídac Surís, Carl Vondrick
CVPR 2023
Paper Project Page

Doubly Right Object Recognition: A Why Prompt for Visual Rationales
Chengzhi Mao, Revant Teotia, Amrutha Sundar, Sachit Menon, Junfeng Yang, Xin Wang, Carl Vondrick
CVPR 2023

Affective Faces for Goal-Driven Dyadic Communication
Scott Geng*, Revant Teotia*, Purva Tendulkar, Sachit Menon, Carl Vondrick
arXiv 2023
Paper Project Page

Understanding Zero-Shot Adversarial Robustness for Large-Scale Models
Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, Carl Vondrick
ICLR 2023


Adversarially Robust Video Perception by Seeing Motion
Lingyu Zhang*, Chengzhi Mao*, Junfeng Yang, Carl Vondrick
arXiv 2022
Paper Project Page

Task Bias in Vision-Language Models
Sachit Menon*, Ishaan Preetam Chandratreya*, Carl Vondrick
arXiv 2022

Private Multiparty Perception for Navigation
Hui Lu, Mia Chiquier, Carl Vondrick
NeurIPS 2022
Paper Project Page Code

Representing Spatial Trajectories as Distributions
Dídac Surís, Carl Vondrick
NeurIPS 2022
Paper Project Page

Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse
Sachit Menon, David Blei, Carl Vondrick
UAI 2022

Revealing Occlusions with 4D Neural Fields
Basile Van Hoorick, Purva Tendulkar, Dídac Surís, Dennis Park, Simon Stent, Carl Vondrick
CVPR 2022 (Oral)
Paper Project Page Talk

Globetrotter: Connecting Languages by Connecting Images
Dídac Surís, Dave Epstein, Carl Vondrick
CVPR 2022 (Oral)
Paper Project Page Code

Causal Transportability for Visual Recognition
Chengzhi Mao*, Kevin Xia*, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick
CVPR 2022

It's Time for Artistic Correspondence in Music and Video
Dídac Surís, Carl Vondrick, Bryan Russell, Justin Salamon
CVPR 2022
Paper Project Page

UnweaveNet: Unweaving Activity Stories
Will Price, Carl Vondrick, Dima Damen
CVPR 2022

There is a Time and Place for Reasoning Beyond the Image
Xingyu Fu, Ben Zhou, Ishaan Preetam Chandratreya, Carl Vondrick, Dan Roth
ACL 2022 (Oral)
Paper Code + Data

Real-Time Neural Voice Camouflage
Mia Chiquier, Chengzhi Mao, Carl Vondrick
ICLR 2022 (Oral)
Paper Project Page Science

Discrete Representations Strengthen Vision Transformer Robustness
Chengzhi Mao, Lu Jiang, Mostafa Dehghani, Carl Vondrick, Rahul Sukthankar, Irfan Essa
ICLR 2022


Full-Body Visual Self-Modeling of Robot Morphologies
Boyuan Chen, Robert Kwiatkowski, Carl Vondrick, Hod Lipson
Science Robotics 2022
PaperProject Page Code

The Boombox: Visual Reconstruction from Acoustic Vibrations
Boyuan Chen, Mia Chiquier, Hod Lipson, Carl Vondrick
CoRL 2021
Paper Project Page Video Overview

Adversarial Attacks are Reversible with Natural Supervision
Chengzhi Mao, Mia Chiquier, Hao Wang, Junfeng Yang, Carl Vondrick
ICCV 2021
Paper Code

Dissecting Image Crops
Basile Van Hoorick, Carl Vondrick
ICCV 2021
Paper Code

Learning the Predictability of the Future
Dídac Surís*, Ruoshi Liu*, Carl Vondrick
CVPR 2021
Paper Project Page Code Models Talk

Generative Interventions for Causal Learning
Chengzhi Mao, Amogh Gupta, Augustine Cha, Hao Wang, Junfeng Yang, Carl Vondrick
CVPR 2021
Paper Code

Learning Goals from Failure
Dave Epstein, Carl Vondrick
CVPR 2021
Paper Project Page Data Code Talk

Visual Behavior Modelling for Robotic Theory of Mind
Boyuan Chen, Carl Vondrick, Hod Lipson
Scientific Reports 2021
Paper Project Page


Listening to Sounds of Silence for Speech Denoising
Ruilin Xu, Rundi Wu, Yuko Ishiwaka, Carl Vondrick, Changxi Zheng
NeurIPS 2020
Paper Project Page

Multitask Learning Strengthens Adversarial Robustness
Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, Carl Vondrick
ECCV 2020 (Oral)

We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos
Alex Andonian, Camilo Fosco, Mathew Monfort, Allen Lee, Carl Vondrick, Rogerio Feris, Aude Oliva
ECCV 2020
Paper Project Page

Learning to Learn Words from Visual Scenes
Dídac Surís*, Dave Epstein*, Heng Ji, Shih-Fu Chang, Carl Vondrick
ECCV 2020
Paper Project Page Code Talk

Oops! Predicting Unintentional Action in Video
Dave Epstein, Boyuan Chen, Carl Vondrick
CVPR 2020
Paper Project Page Data Code Talk


Metric Learning for Adversarial Robustness
Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray
NeurIPS 2019
Paper Code

VideoBERT: A Joint Model for Video and Language Representation Learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid
ICCV 2019
Paper Blog

Multi-level Multimodal Common Semantic Space for Image-Phrase Grounding
Hassan Akbari, Svebor Karaman, Surabhi Bhargava, Brian Chen, Carl Vondrick, Shih-Fu Chang
CVPR 2019
Paper Code

Relational Action Forecasting
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, Cordelia Schmid
CVPR 2019 (Oral)


Tracking Emerges by Colorizing Videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy
ECCV 2018
Paper Blog

The Sound of Pixels
Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh McDermott, Antonio Torralba
ECCV 2018
Paper Project Page

Actor-centric Relation Network
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Kevin Murphy, Rahul Sukthankar, Cordelia Schmid
ECCV 2018


Following Gaze in Video
Adria Recasens, Carl Vondrick, Aditya Khosla, Antonio Torralba
ICCV 2017

Generating the Future with Adversarial Transformers
Carl Vondrick, Antonio Torralba
CVPR 2017
Paper Project Page

Cross-Modal Scene Networks
Yusuf Aytar*, Lluis Castrejon*, Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
PAMI 2017
Paper Project Page

See, Hear, and Read: Deep Aligned Representations
Yusuf Aytar, Carl Vondrick, Antonio Torralba
arXiv 2017
Paper Project Page


Generating Videos with Scene Dynamics
Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
NeurIPS 2016
Paper Project Page Code NBC Scientific American New Scientist MIT News

Anticipating Visual Representations with Unlabeled Video
Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
CVPR 2016 (Spotlight)
Paper Project Page NPR CNN AP Wired Stephen Colbert MIT News

Predicting Motivations of Actions by Leveraging Text
Carl Vondrick, Deniz Oktay, Hamed Pirsiavash, Antonio Torralba
CVPR 2016
Paper Dataset

Learning Aligned Cross-Modal Representations from Weakly Aligned Data
Lluis Castrejon*, Yusuf Aytar*, Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
CVPR 2016
Paper Project Page Demo

Visualizing Object Detection Features
Carl Vondrick, Aditya Khosla, Hamed Pirsiavash, Tomasz Malisiewicz, Antonio Torralba
IJCV 2016
Paper Project Page Slides MIT News


Do We Need More Training Data?
Xiangxin Zhu, Carl Vondrick, Charless C. Fowlkes, Deva Ramanan
IJCV 2015
Paper Dataset

Learning Visual Biases from Human Imagination
Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba
NeurIPS 2015
Paper Project Page Technology Review

Where are they looking?
Adria Recasens*, Aditya Khosla*, Carl Vondrick, Antonio Torralba
NeurIPS 2015
Paper Project Page Demo


Assessing the Quality of Actions
Hamed Pirsiavash, Carl Vondrick, Antonio Torralba
ECCV 2014
Paper Project Page


HOGgles: Visualizing Object Detection Features
Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba
ICCV 2013 (Oral)
Paper Project Page Slides MIT News


Do We Need More Training Data or Better Models for Object Detection?
Xiangxin Zhu, Carl Vondrick, Deva Ramanan, Charless C. Fowlkes
BMVC 2012
Paper Dataset

Efficiently Scaling Up Crowdsourced Video Annotation
Carl Vondrick, Donald Patterson, Deva Ramanan
IJCV 2012
Paper Project Page


Video Annotation and Tracking with Active Learning
Carl Vondrick, Deva Ramanan
NeurIPS 2011
Paper Project Page


Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces
Carl Vondrick, Deva Ramanan, Donald Patterson
ECCV 2010
Paper Project Page


  • Computer Vision II (Summer 2021, Spring 2022-2024)
  • Computer Vision I (Fall 2018-2019)
  • Advanced Computer Vision (Spring 2019)
  • Machine Learning Frontiers (Fall 2024)
  • Representation Learning (Fall 2020-2022)


  • National Science Foundation
  • Defense Advanced Research Projects Agency
  • Toyota Research Institute
  • Amazon Research
  • Google