Emilie Yu
@emxtyu
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@emxtyu on the other site
Joined March 2013
Excited to share our latest work on bringing the concept of layers to 3D painting in VR! #SIGGRAPH2024
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I also gather some related toughts in this appendix of the #LearnWebGPU programming guide: "Keep in mind lower-end devices"
eliemichel.github.io
This documentation walks you through the use of the WebGPU graphics API to create native 3D applications in C++ from scratch, for Windows, Linux and macOS.
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The fact is that research papers are mostly tested on the very top tier of GPUs available the year they are published (and publication takes time, so they're actually even more ahead).
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Is it because researchers only work with recent high end #GPU that everybody has to renew their hardware so fast or is the other way around? Causality is hard to prove, but the correlation is a fact! And we have plenty more figures to show at our #SIGGRAPH2025 talk next summer!
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Apply now to be part of the 2025 cohort of rising stars in computer graphics 🌟 This is a mentoring program with workshops co-located with Siggraph, for students and post-docs of under-represented genders in CG research Deadline for submissions: Apr 18 https://t.co/MfJsRN66o1
wigraph.org
The Rising Stars in Computer Graphics program is designed to bring new perspectives to computer graphics research. This is a two-year program that will be co-located with SIGGRAPH 2025 and SIGGRAPH...
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Hi all, I’m on the job market for industry research scientist or TT faculty positions starting Summer 2025. Interested in roles related to HCI, XR, Eye Tracking, Adaptive Interfaces, and Human-AI Interaction. Please reach out if hiring or aware of any positions! RT appreciated!
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Work done with @fanny_uoft @karansher and Adrien Bousseau. This wouldn't have been possible without precious insights and time invested by our study participants! The @QuillSmoothstep community is lovely, thank you so much!
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For more details, please check out our paper or come see our talk at Siggraph in Denver! >> https://t.co/868rmEbeyt We also open-source the code
github.com
Contribute to graphdeco-inria/3dlayers development by creating an account on GitHub.
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We invited a few VR artists to try the tool and I also painted some things myself! So I can say first-hand, I think it's pretty fun :)
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By letting artists work on a stack of apperance layers that define colors on top of a substrate layer that defines geometry, we can get many cool things! ✔️ non-destructive editing ✔️ color effects (blend modes, opacity gradients)
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That issue stems from the fact that brush strokes in Quill define both the 3D geometry and the color to be rendered. Our key idea is to decouple geometry and color, such that these two components can be edited independently with stroke-based interactions.
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... or add new strokes slightly in front of the previous ones -- hard to make it work from all viewpoints in 3D.
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A specific pain point we identified was on how to edit colors of 3D strokes. In Quill, you have to either apply new colors as vertex colors -- a bit blurry and hard to control...
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VR painting tools like Quill are great -- love that software! -- but they're still in the early days of defining what a painting experience in 3D could be.
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We got the chance to chat with some brilliant Quill artists to understand VR painting workflows better. Turns out, in VR painting you can essentially paint all light and shadow effects, and it looks great. Here's a beautiful example by @NickTheLadd
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Hope some people find it useful and have as much fun fiddling with is as I did 😊
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We could not release the full pipeline to convert your own video clips to video+cameras+depth, but we do describe pretty extensively our internal format. We also provide all preprocessed videos from our paper's results to try out the system!
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So we finally managed to release the code
github.com
Contribute to adobe-research/VideoDoodles development by creating an account on GitHub.
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Time to share our #SIGGRAPH2024 paper "N-Dimensional Gaussians for Fitting of High Dimensional Functions"! We show that N-Dimensional Gaussian mixtures can be optimized to handle high dimensional inputs like MLPs with a fraction of the training times. https://t.co/4edn0ZFwPr
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