February 13, 2026
Neural nets, pixel drama
Adventures in Neural Rendering
Tiny neural brains for prettier pixels — fans hype, skeptics squint, and everyone begs for better visuals
TLDR: A graphics programmer tried tiny neural nets to pack and polish game visuals, sharing early results and trade-offs. The comments lit up: hands-on devs want clear visualizations of what these nets learn, while skeptics demand proof and benchmarks—making neural rendering feel exciting, but not yet convincing.
A graphics dev just dropped a playful deep dive into using tiny neural nets (think mini “brains”) to make game graphics look better—compressing textures, helping lighting, even smoothing jagged edges. It’s not a tutorial, more like a diary of tinkering with simple networks and trying tricks like LeakyReLU (a math switch) to learn faster, plus a reality check that these nets still eat memory—even small ones stack up to thousands of numbers. The community reaction? A glorious split-screen. The tinkerers are clapping: “Tiny nets in shaders? Inject it into my GPU!” One shader dev begged for field maps—basically pictures that show what the net is actually doing—asking how to visualize anything beyond 2D without just slicing it to bits. Translation: cool idea, but show us the pictures. Meanwhile, the skeptics rolled in calling it “black-box sprinkles,” demanding error heatmaps and real benchmarks before they believe the magic. Jokes flew about “9155 float numbers” sounding like a grocery receipt from the Matrix. Some memed that neural nets are the new duct tape; others warned that if we can’t see what it’s learning, we’re just throwing math at vibes. Want more background? The author points to friendly intros like Crash Course in Deep Learning. Verdict: promising, messy, and everyone wants proof-on-a-picture.
Key Points
- •Neural networks are being applied to rendering tasks beyond antialiasing and upscaling, including texture compression, material representation, and indirect lighting.
- •The author experimented with small MLPs to encode data in rendering, demonstrating a 3-3-3-1 architecture and fully connected layers.
- •Activation functions discussed include ReLU and LeakyReLU; LeakyReLU with small alpha (e.g., 0.01) was used and observed to speed learning.
- •Storage needs are quantified: a 3-3-3-1 MLP requires 28 floats, while a larger 9-input, three 64-node hidden layers, 3-output MLP needs 9,155 floats; reduced precision like fp16 can lower memory.
- •Training is summarized at a high level: start with random parameters, perform forward propagation (inference), compute error via a loss function, and update weights using gradients.