Reflecting Process Expertise in Procedural Material Generation

Procedural material creation underpins applications in digitalcontent creation, visual effects, and 3D asset design. Achieving high-quality results requires more than reproducing node graphs—it demandsunderstanding the process by which experts construct materials. We for-mulate procedural material generation as retrieval-time process reasoningover expert demonstrations, elevating process to a first-class represen-tation beyond graph-only synthesis. Concretely, we represent expertworkflows as process traces: textual records of construction steps, param-eters, and design intent. To instantiate this idea, we use a pretrainedLLM-based ProcessSynthesizer to synthesize a process trace alignedwith a user’s intent and a pretrained LLM-based Compiler to groundthe process trace into an executable Blender material graph. Becauseprocedural expertise is most naturally conveyed through demonstrations,we leverage tutorial videos as a source of process knowledge and extracttextual, LLM-compatible traces using automated video analysis tools.In an expert study with five Blender artists (avg. 7.5 years of expe-rience), materials generated by reflecting expert demonstrations werefound to produce workflows requiring fewer edits, and more closely matchprofessional design strategies than methods operating solely on staticartifacts. A user study with 150 participants further shows that ourapproach achieves superior generation and editing performance comparedto prior procedural systems. All code, models, and data will be availableat https://materialapprentice.github.io.