David Fleet
Professor
Studies generative models, video understanding, physics-based vision and cryo-EM reconstruction.
David Fleet's lab at Toronto works on 3D vision & neural rendering, video understanding and generative & foundation models. 21 papers since 2023, 13 at top venues, mostly ICCV and ICLR. 17 alumni, most often to Google and Apple. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 62%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 17
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 4 papers2025 · T1 4
- 7 papers2024 · T1 3
- 10 papers2023 · T1 6
T1 venuesOtherVenues: ICCV, ICLR, Nature Methods, NeurIPS, TMLR
Research focuspaper titles that use each topic's words
- Generative & foundation models6 papers
- 3D vision & neural rendering1 paper
- Video understanding1 paper
- studies generative0 papers
- physics-based0 papers
Paper list
From the lab homepage.
20254 papers
- ICLR (T1)Controlling space and time with diffusion models (opens in a new tab)
- AAAI (T1)High-resolution frame interpolation with patch-based cascaded diffusion (opens in a new tab)
- ICCV (T1)RoMo: Robust motion segmentation improves structure from motion (opens in a new tab)
- TOG (T1)SpotlessSplats: Ignoring distractors in 3D Gaussian splatting (opens in a new tab)
20247 papers
- IEEE Journal of Biomedical and Health InformaticsA personalized video-based hand taxonomy: Application for individuals with spinal cord injury (opens in a new tab)
- NeurIPS (T1)CryoSPIN: Improving ab-initio Cryo-EM reconstruction with semi-amortized pose inference (opens in a new tab)
- Journal of Structural Biology, 216(2):108073Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections (opens in a new tab)
- ICLR (T1)Directly fine-tuning diffusion models on differentiable rewards (opens in a new tab)
- TMLR (T1)Greedy growing enables high-resolution pixel-based diffusion models (opens in a new tab)
- NEJM AITowards generalist biomedical AI (opens in a new tab)
- ECCV WorkshopZero-shot metric depth with a field-of-view conditioned diffusion model (opens in a new tab)
202310 papers
- Nature Methods (T1)3DFlex: Determining structure and motion of flexible proteins from cryo-EM (opens in a new tab)
- ICCV (T1)Generalist framework for panoptic segmentation of images and videos (opens in a new tab)
- Neurorehabilitation and Neural Repair, 15459683231177601Grasp analysis in the home environment as a measure of hand function after cervical spinal cord injury (opens in a new tab)
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [* Highlight Paper *]Imagen Editor and EditBench: Advancing and evaluating text-guided image inpainting (opens in a new tab)
- Nature Methods (T1)Mapping the motion and structure of flexible proteins from cryo-EM data (opens in a new tab)
- Nature Biomedical Engineering, 1-24Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging (opens in a new tab)
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [* Highlight Paper *]RobustNeRF: Ignoring Distractors with Robust Losses (opens in a new tab)
- ICML (T1)Scalable adaptive computation for iterative generation (opens in a new tab)
- TMLR (T1)Synthetic data from diffusion models improves ImageNet classification (opens in a new tab)
- NeurIPS (T1)The surprising effectiveness of diffusion models for optical flow and monocular depth estimation (opens in a new tab)
Where alumni went17 listed, 69% to industry
Industry 11Academia 5
Most often to
- Apple
- Borealis AI
Also to
- University of Hong Kong
- University of Ottawa
- University of Toronto
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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