Harmonizer

Harmonizer | Model Card Paper** | **Project Page** | **Code** | **Model** | **Data** Description Harmonizer is a single-step image diffusion model trained as an online generative enhancer for neural-reconstruction image and video renderings. It transforms imperfect novel-view renderings produced by Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) reconstructions into temporally consistent outputs that are closer to real captures, while correcting illumination, shadow, and reconstruction-artifact issues that arise when dynamic objects are composited into reconstructed scenes. Harmonizer supports two operation modes: **Offline mode:** Used during the reconstruction phase to clean up pseudo-training views rendered from the reconstruction, then distill them back into 3D. This enhances underconstrained regions and improves overall 3D representation quality. **Online mode:** Acts as a single-step neural enhancer during simulation or inference. It harmonizes color and lighting, reconstructs missing or inconsistent shadows for inserted dynamic objects, and removes residual reconstruction artifacts from imperfect 3D supervision and current reconstruction-model capacity limits. Harmonizer is designed as a single model compatible with both NeRF and 3DGS representations. The model was trained on data curated with 3DGUT-based reconstructions and is adaptable to Gaussian Splatting scenes. License/Terms of Use Governing Terms Use of this model is governed by the NVIDIA Open Model License Agreement. Deployment Geography: Global Release Management The model artifacts are released in this repository. Training and inference code is available from the Harmonizer GitHub repository. The associated dataset is available from nvidia/Harmonizer-Dataset. Use Case Harmonizer is intended for Physical AI developers looking to enhance and harmonize neural-reconstruction pipelines for autonomous-vehicle simulation. The model takes an image or image sequence as input and outputs a harm