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🚀 Introducing MegaFlow: Zero-Shot Large Displacement Optical Flow! 🔥 Code, models, and demo — all available now! 🎯 Large displacement motion estimation (optical flow, point tracking) has been a long-standing challenge. MegaFlow proposes a simple solution by leveraging pre-trained vision priors. Combined with a global matching formulation, we tackle...

32,340 Aufrufe • vor 7 Monaten •via X (Twitter)

10 Kommentare

Profilbild von Haofei Xu
Haofei Xuvor 7 Monaten

🌐 Project: 📄 Paper: 💻 Code: This project was done by our truly exceptional Master's student @dingxi39482 (an incoming PhD student at ETH and MPI), together with amazing @FangjinhuaWang and @mapo1 !

Profilbild von Haofei Xu
Haofei Xuvor 7 Monaten

A little backstory: we developed GMFlow (CVPR 2022, and UniMatch (TPAMI 2023, about 4 years ago. It's incredibly exciting to see these old ideas shine again, empowered by modern architectures and tools. It’s equally rewarding to watch young students grow, and I'm so happy to be able to support their research journey!

Profilbild von Taro Bushidō
Taro Bushidōvor 7 Monaten

Finally, zero-shot large displacement that generalizes. The missing piece for robust world models. No domain gaps, no retraining.

Profilbild von Felix Wimbauer
Felix Wimbauervor 7 Monaten

GMFlow rules. I am sure this one will too!

Profilbild von Haofei Xu
Haofei Xuvor 7 Monaten

Haha thanks Felix!

Profilbild von hamuz
hamuzvor 6 Monaten

No way, what a crazy coincidence! I actually started working on a similar project last month, and I’m using GH200 too! The only difference is I’m aiming Mamba-based approach. Your work looks awesome, I’m definitely going to dive deeper into it. Amazing job!

Profilbild von Haofei Xu
Haofei Xuvor 6 Monaten

Wow thank you so much! Great minds think alike haha. Your approach sounds super exciting, and I'm looking forward to seeing what you build with it! Best of luck with your project!

Profilbild von Kentskooking
Kentskookingvor 6 Monaten

@Machinedelusion This is so cool! Thank you!

Profilbild von Haotian Xue
Haotian Xuevor 7 Monaten

How fast is it?

Profilbild von Haofei Xu
Haofei Xuvor 6 Monaten

Hi, it runs at ~0.3s on an RTX 4090 at 540 × 960 resolution.

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AK

123,771 Aufrufe • vor 2 Jahren

Check out our #PAMI paper with code "Dense Continuous-Time Optical Flow from Event Cameras," where we show how to regress *continuous-time* trajectories of every pixel from event cameras alone or events plus frames! The key idea is to iteratively estimate per-pixel polynomials using a recurrent lookup and update scheme. Paper: Code: DOI: We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. We show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous time via parameterized Bézier curves. To achieve this, we build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. To train and evaluate our model, we introduce a synthetic dataset (MultiFlow) that features moving objects and ground truth trajectories for every pixel. Our quantitative experiments suggest that our method successfully predicts pixel trajectories in continuous time and is competitive in the traditional two-view pixel displacement metric on MultiFlow and DSEC-Flow. Open source code and datasets are released to the public. Kudos to Mathias Gehrig Manasi Muglikar

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