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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 次观看 • 7 个月前 •via X (Twitter)

10 条评论

Haofei Xu 的头像
Haofei Xu7 个月前

🌐 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 !

Haofei Xu 的头像
Haofei Xu7 个月前

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!

Taro Bushidō 的头像
Taro Bushidō7 个月前

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

Felix Wimbauer 的头像
Felix Wimbauer7 个月前

GMFlow rules. I am sure this one will too!

Haofei Xu 的头像
Haofei Xu7 个月前

Haha thanks Felix!

hamuz 的头像
hamuz7 个月前

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!

Haofei Xu 的头像
Haofei Xu7 个月前

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!

Kentskooking 的头像
Kentskooking6 个月前

@Machinedelusion This is so cool! Thank you!

Haotian Xue 的头像
Haotian Xue7 个月前

How fast is it?

Haofei Xu 的头像
Haofei Xu7 个月前

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

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Meta just announced FlowVid Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis paper page: Diffusion models have transformed the image-to-image (I2I) synthesis and are now permeating into videos. However, the advancement of video-to-video (V2V) synthesis has been hampered by the challenge of maintaining temporal consistency across video frames. This paper proposes a consistent V2V synthesis framework by jointly leveraging spatial conditions and temporal optical flow clues within the source video. Contrary to prior methods that strictly adhere to optical flow, our approach harnesses its benefits while handling the imperfection in flow estimation. We encode the optical flow via warping from the first frame and serve it as a supplementary reference in the diffusion model. This enables our model for video synthesis by editing the first frame with any prevalent I2I models and then propagating edits to successive frames. Our V2V model, FlowVid, demonstrates remarkable properties: (1) Flexibility: FlowVid works seamlessly with existing I2I models, facilitating various modifications, including stylization, object swaps, and local edits. (2) Efficiency: Generation of a 4-second video with 30 FPS and 512x512 resolution takes only 1.5 minutes, which is 3.1x, 7.2x, and 10.5x faster than CoDeF, Rerender, and TokenFlow, respectively. (3) High-quality: In user studies, our FlowVid is preferred 45.7% of the time, outperforming CoDeF (3.5%), Rerender (10.2%), and TokenFlow (40.4%).

AK

123,771 次观看 • 2 年前

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

Davide Scaramuzza

12,692 次观看 • 2 年前