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expanding on my WebGPU motion tracking effect work this time with optical flow to visualize the motion of pixels as directional vectors in real time :)

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Maxime

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19,931 görüntüleme • 1 ay önce •via X (Twitter)

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blob tracking effect built with WebGPU had fun playing with motion-based effects alongside sound over the past few weeks :)
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blob tracking effect built with WebGPU had fun playing with motion-based effects alongside sound over the past few weeks :)

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Netflix just dropped Go-with-the-Flow Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
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Time to put those deformable voxels to work. Here is a very quick softbody test. There are some artefacts and missing motion vectors but there's potential!
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Time to put those deformable voxels to work. Here is a very quick softbody test. There are some artefacts and missing motion vectors but there's potential!

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UE5.6 AI Real-Time Motion Generation Plugin — powered by a 1-billion-parameter motion model trained on NVIDIA CUDA. It runs locally in real time with 8GB of VRAM and converts webcam or video footage into real-time XYZ skeletal point data and rotation values.

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UE5.6 AI Real-Time Motion Generation Plugin — powered by a 1-billion-parameter motion model trained on NVIDIA CUDA. It runs locally in real time with 8GB of VRAM and converts webcam or video footage into real-time XYZ skeletal point data and rotation values.
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UE5.6 AI Real-Time Motion Generation Plugin — powered by a 1-billion-parameter motion model trained on NVIDIA CUDA. It runs locally in real time with 8GB of VRAM and converts webcam or video footage into real-time XYZ skeletal point data and rotation values.

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I forgot to share this reel of my work I had to make for the Young Guns ceremony earlier this month—it was really fun to see some of my work in motion for the first time! (By "had to make" I obviously mean "had to hire someone to make"—huge ty to motion designer Devon Burgoyne!)
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I forgot to share this reel of my work I had to make for the Young Guns ceremony earlier this month—it was really fun to see some of my work in motion for the first time! (By "had to make" I obviously mean "had to hire someone to make"—huge ty to motion designer Devon Burgoyne!)

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Hooke's law in motion: a dancer's joints anchor a real-time spring-mass lattice. Every extension, contraction, and twist propagates tension through the system; rendering these hypnotic, radiating dashed vectors as emergent force fields.
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Hooke's law in motion: a dancer's joints anchor a real-time spring-mass lattice. Every extension, contraction, and twist propagates tension through the system; rendering these hypnotic, radiating dashed vectors as emergent force fields.

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Tracking Everything Everywhere All at Once paper page: present a new test-time optimization method for estimating dense and long-range motion from a video sequence. Prior optical flow or particle video tracking algorithms typically operate within limited temporal windows, struggling to track through occlusions and maintain global consistency of estimated motion trajectories. We propose a complete and globally consistent motion representation, dubbed OmniMotion, that allows for accurate, full-length motion estimation of every pixel in a video. OmniMotion represents a video using a quasi-3D canonical volume and performs pixel-wise tracking via bijections between local and canonical space. This representation allows us to ensure global consistency, track through occlusions, and model any combination of camera and object motion. Extensive evaluations on the TAP-Vid benchmark and real-world footage show that our approach outperforms prior state-of-the-art methods by a large margin both quantitatively and qualitatively.
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Tracking Everything Everywhere All at Once paper page: present a new test-time optimization method for estimating dense and long-range motion from a video sequence. Prior optical flow or particle video tracking algorithms typically operate within limited temporal windows, struggling to track through occlusions and maintain global consistency of estimated motion trajectories. We propose a complete and globally consistent motion representation, dubbed OmniMotion, that allows for accurate, full-length motion estimation of every pixel in a video. OmniMotion represents a video using a quasi-3D canonical volume and performs pixel-wise tracking via bijections between local and canonical space. This representation allows us to ensure global consistency, track through occlusions, and model any combination of camera and object motion. Extensive evaluations on the TAP-Vid benchmark and real-world footage show that our approach outperforms prior state-of-the-art methods by a large margin both quantitatively and qualitatively.

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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 both optical flow and point tracking in a unified structure, delivering incredibly strong results. 🏆 SOTA zero-shot performance across multiple benchmarks and in-the-wild videos. Check out the links below to see it in action! 👇
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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 both optical flow and point tracking in a unified structure, delivering incredibly strong results. 🏆 SOTA zero-shot performance across multiple benchmarks and in-the-wild videos. Check out the links below to see it in action! 👇

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PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.

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