JLPM

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JLPM

JLPM

@JLPMdev

📐 Computer Graphics, Animation and VR 💻 PhD Student 🎮 GameDev 🎥 I have a game dev YouTube Channel (Spanish) with +120k subs!

Barcelona, España Katılım Haziran 2013
109 Takip Edilen794 Takipçiler
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Eduardo Alvarado
Eduardo Alvarado@roboonaut·
Have you ever wondered if we could capture human movement without mocap suits, VR trackers, or camera setups? What if all you needed was a pair of everyday insoles? 👟 Introducing Step2Motion, accepted to #Eurographics2026! 📝Project Page: vcai.mpi-inf.mpg.de/projects/Step2… 🧵👇
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Matt Hackett 📙
Matt Hackett 📙@richtaur·
📈 The Steam Dev Cheat Sheet 🏆 A dense collection of best practices, magic formulas, and Steam knowledge to help #indiedev folks like me. 📺 PLUS a 5:40 video briefly covering each section. Links to everything below: 🧵
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AK@_akhaliq·
Example-based Motion Synthesis via Generative Motion Matching paper page: huggingface.co/papers/2306.00… present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.
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Shen Ye
Shen Ye@shen·
Have been asked a few questions about the new VIVE self tracking tracker, so here's a thread 🧵! We're announcing it at GDC as a developer preview, with launch later in Q3. It's fully self tracking, so doesn't need external sensors or a headset to see it. 👀 (1/x)
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Keenan Crane
Keenan Crane@keenanisalive·
Tiny changes to the order in which you update positions x and velocities v can be the difference between your simulation blowing up or dying down. But for many systems, *symplectic* integrators guarantee energy is preserved forever. Full lecture here: youtube.com/watch?v=Fi9xu3…
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Bilgin Ibryam
Bilgin Ibryam@bibryam·
Git visual, worth a thousand words
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Eduardo Alvarado
Eduardo Alvarado@roboonaut·
"So if you need to rotate something with grace, Quaternions will help you find the right place." #ChatGPT @JLPMdev
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Freya Holmér
Freya Holmér@FreyaHolmer·
the four most common 3D rotation representations
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JLPM
JLPM@JLPMdev·
@roboonaut que miedo empezar jajajaja
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Farhan
Farhan@TheStrokeForge·
Vox Cleaner V2, My Blender add-on is Out now, for Free! Enjoy!
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Alexander Winkler
Alexander Winkler@awinkler_·
This paper shows some of our research at Meta Reality Labs in reconstructing a user's pose from only the sensors of the Quest headset using Reinforcement Learning. authors: with Jungdam Won and Yuting Ye paper: arxiv.org/abs/2209.09391 video: youtu.be/CkTHsz6Ldas 1/4👇
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JLPM
JLPM@JLPMdev·
@y_haoran Best video from the paper!!!
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