Here's Bolt3D: fast feed-forward 3D generation from one or many input images. Diffusion means that generated scenes contain lots of interesting structure in unobserved regions. ~6 seconds to generate, renders in real time.

Project page: szymanowiczs.github.io/bolt3d
Arxiv: arxiv.org/abs/2503.14445

Oops, there was a bug in one of the animations, here's a fixed version.

More activations: composing f(x, λ) exactly reproduces softplus, sigmoid, tanh, relu, and linear activations, and smoothly interpolate between them. This suggests f() could be useful for network architectures, but I haven't explored this. Someone should! (again, happy to help)

Now the cool stuff. f(x, λ) is defined for x>=0, but you can generalize it x<0 inputs by giving them their own λ. Changing (λ-, λ+) values lets you interpolate between sigmoid-/logit-/exp-/log-shaped functions. This family also *exactly* reproduces the ELU activation function!

f'(0.5 * x^2) yields a long list of kernel functions from the literature: rational quadratic kernels, the inverse kernel, the Gaussian/RBF kernel, the quadratic kernel, the multiquadric kernel, the inverse multiquadric kernel, and a non-normalized Student's T-distribution.

If you use those losses as negative log-likelihoods you get a family of PDFs that interpolates between Cauchy, Gaussian, and Epanechnikov distributions. This is again a superset of my CVPR2019 paper, where the new distributions (λ>1) have a finite domain that narrows as λ grows.

Applying f to x^2 yields a nice family of losses that interpolates between Welsch loss, Geman-McClure loss, Cauchy loss, Charbonnier loss, and L2 loss. This is a mild superset of my CVPR2019 paper/loss (the new losses are for λ>1, not sure if they're useful but they look nice).

I just pushed a new paper to arXiv. I realized that a lot of my previous work on robust losses and nerf-y things was dancing around something simpler: a slight tweak to the classic Box-Cox power transform that makes it much more useful and stable. It's this f(x, λ) here:

Here's "A cute dog catching a frisbee in the air, the camera is upside down", mirrored vertically. You get a fun zero-G dog with floppy backwards-bending feet, and a frisbee that keeps trying to fly up into the sky like a helium balloon.