This repository implements Pixie, a research framework for predicting dense physical material fields (density, Young's modulus, Poisson's ratio, material ID) from 2D image observations of 3D objects. Hydra configs drive a multi-stage pipeline: Objaverse object retrieval, Blender rendering, NeRF-based CLIP feature field training (F3RM), Gaussian Splatting training, voxel grid feature extraction, VLM-based or neural material segmentation, and MPM physics simulation via the vendored PhysGaussian codebase. The pipeline.py entry point orchestrates these stages with caching and reproducibility checks, while render.py produces Blender visualizations of segmented physics fields. Real-scene mode extends the same pipeline to captured datasets using COLMAP dataparsers. The repository also contains the full PixieVerse data curation workflow: class-based Objaverse selection using sentence-transformers, large-scale parallel Blender rendering, VLM-based filtering and critique (Gemini/Claude/GPT/Qwen via the vendored vlmx agent framework), and Streamlit tools for human-in-the-loop label correction and simulation validation. The discrete (semantic segmentation) and continuous (property regression) 3D UNet training lives in the vendored Wavelet-Generation submodule, with shared utilities in pixie/training_utils.py and normalization statistics already computed in normalization_stats/. Architecturally this is a hybrid research/application repository: primarily a configuration-driven pipeline framework with CLI entry points, but also a dataset-generation and model-training workflow. It depends heavily on vendored third-party code (nerfstudio, f3rm, PhysGaussian, Wavelet-Generation, vlmx) and shells out to Blender, CUDA rasterizers, and Hugging Face. Quality is research-grade: disciplined Hydra configs, contextual config snapshots, and skip-if-exists caching coexist with hardcoded user-specific paths, commented-out dead code, and a subprocess-heavy orchestration style. Download scripts for pre-trained models and the PixieVerse dataset are included and functional.