Summary of Preserving Identity with Variational Score For General-purpose 3d Editing, by Duong H. Le et al.
Preserving Identity with Variational Score for General-purpose 3D Editing
by Duong H. Le, Tuan Pham, Aniruddha Kembhavi, Stephan Mandt, Wei-Chiu Ma, Jiasen Lu
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Piva method is an optimization-based approach for editing images and 3D models using diffusion models. Building on the Delta Denoising Score (DDS) technique, Piva addresses limitations in DDS by introducing an additional score distillation term that preserves image identity. This results in a more stable editing process, allowing for gradual optimization of NeRF models to match target prompts while retaining input characteristics. The method demonstrates effectiveness in zero-shot image and neural field editing, successfully altering visual attributes, adding structural elements, translating shapes, and achieving competitive results on 2D and 3D editing benchmarks. Piva is compatible with a wide range of pre-trained diffusion models, offering a user-friendly experience without requiring neural field-to-mesh conversion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Piva is a new way to edit pictures and 3D models using special computer programs called diffusion models. The problem with current methods is that they can make important details disappear or become too bright. To fix this, Piva adds an extra step to help keep the original image identity. This makes the editing process more stable and accurate. Piva has been tested on many different types of images and 3D models, and it works well at making changes like changing colors, adding shapes, and translating objects. What’s special about Piva is that it can work with many different pre-trained diffusion models without needing to convert the 3D models into a specific format. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Optimization » Zero shot
