JLPM

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

[JLPM @JLPMdev]

Nuevo vídeo! Siguiendo con la serie de creando videojuegos, os traigo Shipped de Alva Majo (@5ro4) pero en 3D ! 😏

Para ver que tal ha quedado, dejo el vídeo por aquí!
https://t.co/DGnkvQvEvp

#gamedev #devsfromspain #indiedev #unity #unity3d #indiesp

[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: https://t.co/JMxzexgc2M
🧵👇

[mgear @unitycoder_com]

"Motion Matching implementation for Unity" https://t.co/6LnmEbZU4u

[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: 🧵

[AK @_akhaliq]

Example-based Motion Synthesis via Generative Motion Matching

paper page: https://t.co/rP1oH6OtoT

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.

[Cataxis 🇲🇽🏳️‍⚧️🎮 PLAY NOW BREAKOUT EPILEPSIA @Cataxis2]

JLPM canal de @JLPMdev
or cierto, no me abre en pestaña incógnito y está bugueado... Está inactivo desde hace casi un año pero tiene unos vídeos muy "curradoos"

Link: https://t.co/8znuJaNSy3

[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)

[GlobalGameJam @globalgamejam]

🐤▫️
#programmer #gamedev #indiedev #unity

[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: https://t.co/cIf4dwtgXg

[Bilgin Ibryam @bibryam]

Git visual, worth a thousand words

[Eduardo Alvarado @roboonaut]

"So if you need to rotate something with grace,
Quaternions will help you find the right place."
#ChatGPT @JLPMdev

[Freya Holmér @FreyaHolmer]

the four most common 3D rotation representations

[JLPM @JLPMdev]

@roboonaut que miedo empezar jajajaja

[JLPM @JLPMdev]

He sido seleccionado para la beca de doctorado FPU!!! Ahora tengo financiación para seguir investigando en Realidad Virtual y Computer Animation :))

[Ministerio de Universidades @UniversidadGob]

🔴 Publicada la resolución provisional de selección en segunda fase de las ayudas para formación de profesorado universitario (FPU) 2021.

🔗 https://t.co/HNYs1cbnUc

[JLPM @JLPMdev]

@Cataxis2 Gracias :D

[JLPM @JLPMdev]

@Vance_Dyer Graciaaas! <3

[Farhan @TheStrokeForge]

Vox Cleaner V2, My Blender add-on is Out now, for Free! Enjoy!

[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: https://t.co/hpZhuY8PC1
video: https://t.co/BBRGMoHt4t
1/4👇

[Jakob Foerster @j_foerst]

My "How to ML Paper - A brief Guide" (https://t.co/1GyqNcdxDm) is getting visitors again! Good luck with your #ICLR2023 submissions :)

[Daniel Holden @anorangeduck]

My latest blog post is on how we can automatically create looping animations from motion capture data:

https://t.co/qVM4D8rLI0

Web demo here:

https://t.co/dU2LOj1L7p

[JLPM @JLPMdev]

@y_haoran Best video from the paper!!!