artixels Profile Banner
Mike Wong Profile
Mike Wong

@artixels

Followers
2K
Following
9K
Media
398
Statuses
1K

pixel surgeon

latentpark
Joined June 2009
Don't wanna be here? Send us removal request.
@janusch_patas
MrNeRF
21 days
Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting TL;DR: How to freeze a dynamic molecular scene when people inevitably move. Contributions: • A Novel Problem Formulation and Benchmark: We are the first to formally address synthesizing
1
4
39
@ShinjiOgaki
Shinji Ogaki
1 month
今年は2次元のtransient renderingを使った作品を提出しました。 運営の皆様お疲れ様でした。 #レイトレ合宿
3
106
702
@artixels
Mike Wong
2 months
The same stippling method can create some interesting depth of field effect.
@artixels
Mike Wong
4 years
Found a Depth of Field demo code of 2.5D moving stipples; here we slide the image plane through Z. #bluenoise #LagrangianImage #atoMeow
0
1
10
@artixels
Mike Wong
2 months
Wow, a *phenomenal* single author paper by @ryichando https://t.co/qS7GZqKWnT
@twominutepapers
Two Minute Papers
2 months
Every cape is one bug away from becoming a scarf. 🦸‍♂️ But one scientist finally fixed it - millions of ribbons, noodles, and fabrics twisting together. Human ingenuity at its best! What a time to be alive! Full video: https://t.co/kqrnLYt7Hf
1
0
3
@voxagonlabs
Dennis Gustafsson
5 months
My talk from @BetterSoftwareC last week is up on youtube. I present my findings on thread synchronization and job systems that I learned while parallelizing the physics solver.
16
95
731
@artixels
Mike Wong
5 months
🔥😍🔥
@ZephirFXx
Enzo Crema
5 months
I'm thrilled to share the VQVDB trailer 😎 More info : https://t.co/NdAdMKApJM #houdini #ai #ml #openvdb
0
0
0
@artixels
Mike Wong
6 months
artistic exploration via sampling a tiny neural network's memory. curate your own data and train your own model. #diffusion #neural #generative
1
0
5
@artixels
Mike Wong
6 months
Selected more photos from my collection and finished a better dataset for my personal cloudscape model training. At the end, it's the quality of data that matters most for any machine learning solution.
0
0
1
@artixels
Mike Wong
7 months
Spent some time going outdoor to shoot more cloud photos. I quickly used around 100 crops and trained a tiny model. Quite happy with the results.
0
0
2
@artixels
Mike Wong
7 months
The season finale of Lighter Darker is loads of fun and I am a proud owner of a *Physical* copy of Knoll Light Factory but I really want to try Knoll’s Flare Box
@tvaziri
Todd Vaziri
7 months
The first season of the ILM podcast comes to a close with our Lens Flare Spectactular. We're joined by John Knoll and Shannon Tindle to talk about our favorite lens flares from movies, the favorite flares we've made, and much much more. Listen here: https://t.co/ZvoTB5tFEU
0
0
3
@artixels
Mike Wong
7 months
Right-sizing a diffusion model seems important for capturing the different levels of variation of a target distribution. This particular generated sample truly captures what I like about the training set. There are so many dimensions of art making with Machine Learning.
0
0
0
@artixels
Mike Wong
7 months
Attention is *not always* our need Removed the Attention modules from the UNet Halved the filter kernel numbers A more compact UNet learned faster and an improved distribution, i.e. better unseen samples.
0
0
3
@artixels
Mike Wong
7 months
blossom memories
0
0
3
@artixels
Mike Wong
7 months
A quick test of using the tiny diffusion model trained last week for 2K generation. The result is meaningful and reasonable, likely because of the data distribution nature of cloud images. It's possible to create a realtime infinite scrolling panorama with this tiny model.
1
1
3
@artixels
Mike Wong
8 months
Trying to see how a tiny diffusion model will mix or separate the distribution of two flower images during training. This one is a nice in-between.
0
0
0
@artixels
Mike Wong
8 months
Their nicely written blog https://t.co/QxVuRRXuVY
0
0
0
@artixels
Mike Wong
8 months
Training a tiny diffusion model using my previous work (12 images) as a dataset. The ability to sample and visualize a distribution of your own aesthetics is pretty cool. I am using the elegant Iterative 𝛼-(de)Blending method (IADB) proposed by Heitz et al (see reply)
1
0
1
@artixels
Mike Wong
8 months
0
0
0
@artixels
Mike Wong
8 months
only a few epochs and the samples feel a bit impressionistic, kind of nice.
0
0
0
@artixels
Mike Wong
8 months
Epoch #100 Learning to sample again
0
0
1