Heightfield advection is a powerful technique in Houdini for simulating complex terrain evolution and fluid-like effects.

Intrinsic UVs in Houdini are a powerful way to access geometric properties without extra attributes. Depending on the primitive type, they are interpreted differently.

Houdini Tokyo HIVE video is live:

I wanted to share a quick visual comparison between the SideFX recording of my Houdini HIVE Tokyo 2025 session “Adaptive Fracture Synthesis and Propagation in VEX using OpenSubdiv Limit Surface Derivatives” and my own local QHD capture.

The SideFX version is completely fine for watching the presentation, but my recording was captured directly from my display source with no compression loss, so it preserves a lot more clarity across the entire presentation. This includes all geometry details, colors, UI elements, and text. This is one of the main reasons I am preparing my own edited cuts.

The attached images show the difference between the two versions.

The full END-JPN cut will be posted next, followed later by the English-only version. These edited cuts will be available exclusively to paid members on Patreon [www.patreon.com].

Pragmatic VEX: Volume 1: Canonical Version Update

Over the past 3 months, I’ve completely rebuilt the course.

Every video has been re-edited for better flow, pacing, and clarity.
Explanations have been refined, key sections restructured, and the overall learning experience has been significantly improved.

This is now the canonical version of the course.
It replaces the previous version.

• All lessons reworked for tighter structure and better comprehension
• Redesigned concept visuals and supporting material for clearer presentation
• New annotations to reinforce important ideas
• Help pages redesigned with proper styling instead of raw captures
• Updated to align with current Houdini workflows

The goal was to reduce friction and improve understanding while keeping the full technical depth intact.

If you already own the course, this update is available to you.
If you were considering it before, this is the right time to start.

More content is already in progress and will be released this year.

Hi Do you still have the free version available that was previously onside fx website? Thank you

alena.bejenarou
Hi Do you still have the free version available that was previously onside fx website? Thank you

Hi, yes it's on the SideFX page:
https://www.sidefx.com/tutorials/pragmatic-vex-volume-1-free-samples/ [www.sidefx.com]

But it doesn't include the latest major update released.

Polar Inversion COP [www.patreon.com]

I have been experimenting with Houdini’s new Copernicus network and started porting some of my existing COP tools into custom OpenCL COP nodes.

The first one is a Polar Inversion filter implemented directly as an OpenCL COP node.

The core mapping is based on radial inversion around a center point:

scale = radius² / distance²

In practice, the important part is not the formula itself, but making it stable and usable as an image filter.

The implementation uses inverse mapping, where each output pixel traces back to the corresponding source position. Near the inversion center, the denominator is clamped using a pixel-footprint floor, with an optional softness floor. This avoids the unstable singularity you would otherwise get from dividing by a near-zero distance.

For sampling, the node uses deterministic subpixel supersampling and Houdini’s rectangular filtered sampling through "imageSampleRect". The filter footprint is currently based on the output pixel footprint, which gives better reconstruction than a naive point lookup while still remaining predictable and fast.

This is the direction I want to take with Copernicus: converting my existing COP nodes and implementing more advanced image filters, compositing operators, and procedural transformations that are not currently available in Houdini.

The goal is to build a deeper image-processing toolset inside Houdini, using the same philosophy I use in my VEX work: low-level control, practical performance, and tools designed around real production needs.

Following up on my earlier COP filtering work, I added another high-quality method to OSSI (Output-Space Subpixel Integration) and compared it with the updated Houdini 22 Swirl COP.

H22 is a substantial improvement over H21 in terms of smoothing the swirl lines, but the central singular region is still visibly unstable and poorly resolved. Some broader curved regions also become noticeably softened. This is consistent with Box filtering over a rectangular area footprint estimated from finite differences of the warp, whereas OSSI integrates how the nonlinear deformation varies across the output pixel.

The new method uses adaptive antithetic Owen-scrambled Sobol sampling. Sobol provides low-discrepancy progressive coverage, Owen scrambling changes the sequence per pixel to reduce visible sampling structure, and pairing each offset with its opposite keeps every completed pair exactly centered.

Like my existing Adaptive R2 method, the sample count and integration width respond to the estimated warp difficulty. Adaptive R2 uses a lightweight progressive R2 disk sequence with a hashed per-pixel rotation, while the Owen-Sobol method uses pixel-specific scrambling of the Sobol sequence together with antithetic sample pairs.

The sample budget and integration width are driven by a conservative finite-difference Jacobian envelope and a footprint-scale second-order nonlinearity estimate. Power-of-two pair budgets evaluate the Sobol sequence at its strongest stratification points as sampling increases.

This is a comparison of the complete stock and custom implementations, not an isolated Box-versus-Sobol test, since their center regularization and footprint-planning strategies also differ.