David Lindell

@DaveLindell

Assistant Professor @UofTCompSci Faculty Affiliate @VectorInst

Toronto, ON

davidlindell.com

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David Lindell
@DaveLindell
Aug 10
Many photographs have motion blur, whether from hand shake or movement in the captured scene. Post-training a video model allows us to turn a motion-blurred image into a video sequence that is consistent with the recorded blur. Your blurry photos are worth more than you think!
Sai Tedla
@tedlasai
Aug 10
🌟We introduce a method for revisiting historical photos and bringing them to life. Specifically, our model generates video from motion-blurred images. 📢 Blur2Vid (Transactions on Graphics, SIGGRAPH Asia 2025) Webpage: blur2vid.github.io Paper: arxiv.org/abs/2512.19817
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David Lindell
@DaveLindell
Jul 9
ProxyPose uses video-to-video translation for 6-DoF tracking! A pre-trained video model solves the hardest part of the problem by generating an easily tracked "proxy" object that follows the motion of a query surface region. Then, tracking the proxy object is trivial with OpenCV.
Ruihang Zhang
@ruihangzhang
Jul 8
Introducing 📢📢ProxyPose📢📢 ✨Track 6-DoF motion of any query pixel via video-to-video translation 📰ArXiv: arxiv.org/abs/2607.06555 💻Code: github.com/ruihangzhang97… 🌐Webpage: ruihangzhang97.github.io/proxypose
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David Lindell
@DaveLindell
Jun 20
Conventional coherent lidars are typically difficult to implement and don't recover polarization information. We designed a new type of coherent lidar using an off-the-shelf optical modem, commonly used in telecommunications, to recover depth, velocity, and polarization!
Dongyu Du
@Dongyu_Du
Jun 19
🚀Excited to share our Optica work! New lidar system simultaneously recovers location, speed, and material properties! (🔗dongyu-du.github.io/project/pfwl/) Thanks to all collaborators @AndrewEJXie, Parsa Mirdehghan, Brandon Buscaino,@SeungHwanBaek8, Kiriakos Kutulakos, and @DaveLindell
David Lindell
@DaveLindell
Jun 6
Congratulations, @KellyKZhu! It was great to work with Kelly during her MSc, and I'm excited for her to start her PhD at CMU. See here for all of Kelly's work, and more exciting things to come soon: kellyzhu.ca
U of T Department of Computer Science
@UofTCompSci
Jun 5
Seeing research come to life 👀 Kelly Zhu, MSc graduate in computer science, works across AI, robotics and computer vision — and shares what comes next ↓ uoft.me/cs6
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David Lindell
@DaveLindell
Jun 5
Really cool project led by @HaojunQiu! We show a patch-based image generation method with closed-form diffusion (i.e., analytical denoising—no neural network). It's *super* efficient and even scales to gigapixel generation! #CVPR2026
Haojun Qiu
@HaojunQiu
Jun 5
📢📢📢We introduce Efficient-SID⚡️: training-free single-image diffusion model that generates images by sampling directly from an input image's patch distribution. Our method enables megapixel generation in <1s and scales to gigapixel generation. We also enable stylization,
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