Advancing VFX Workflows with Houdini Solaris in House of the Dragon Season 2
Bibliographic record
Abstract
This work presents a successful real-world transition of a major VFX studio, Rodeo FX, from a multi-software pipeline to a unif ied Solaris and USD-based workflow. Motivated by the growing complexity of episodic and feature film productions, such as Red One and House of the Dragon Season 2, the shift aimed to consolidate disparate departments and eliminate redundant tasks by using Houdini Solaris as the central platform for asset development, Crowd, CFX, FX, lighting, and rendering. The core of this pipeline transformation involved standardizing USD layer stack composition, introducing reusable HDAs and Rodeo Chunks, and automating per-shot workflows using Shotgun event triggers and dispatch graphs. We detail implementation specifics such as the USD Payload Package concept, layer stack auto-generation, and robust automation systems that allowed for efficient iteration, shot synchronization, and cross-departmental collaboration. Finally, we demonstrate production-level results using case studies from House of the Dragon Season 2, including environment builds of King's Landing, Silverwing dragon shading, FX instancing workflows, and automated shot renders. This abstract outlines practical strategies for large studios aiming to migrate to a scalable, artist-friendly, and automation-driven USD pipeline.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".