Add view-consistency correction to an existing text-to-3D generation pipeline like DreamFusion.
Add view-consistency correction to an existing image-to-3D pipeline like Zero-1-to-3.
Reproduce the NeurIPS 2024 Hallo3D paper's experiments.
Study how a multi-modal model can detect hallucinated details across 3D generation views.
| wafer-bob/hallo3d | 0c33/agentic-ai | adennng/stock_strategy_lab | |
|---|---|---|---|
| Stars | 14 | 14 | 14 |
| Language | Python | Python | Python |
| Setup difficulty | hard | hard | hard |
| Complexity | 5/5 | 4/5 | 4/5 |
| Audience | researcher | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires threestudio plus extra extensions, a compatible transformers version, and a single 80GB A100 GPU for training runs.
Hallo3D is the official code release for a research paper accepted at NeurIPS 2024, a major AI research conference. The project tackles a specific problem in AI generated 3D content: when a model builds a 3D object from a text description or an image, it can end up giving the object different, contradictory appearances depending on which angle you look at it from, sometimes called the Janus problem, where a single object appears to have multiple faces or inconsistent features across views. Hallo3D works as a plug in that attaches to existing 3D generation systems rather than replacing them, and it does not require retraining those systems or changing their architecture. It works through three connected steps. First, it aligns the color and texture of the object across different rendered viewpoints using an attention mechanism. Second, it uses a large multi-modal model, specifically LLaVA, to inspect the generated views and detect hallucinated or inconsistent details, turning what it finds into an enhanced negative prompt. Third, it uses that negative prompt to selectively correct the generation, nudging future views back toward consistency without needing to redo the whole process. The method is designed to work across a range of existing 3D generation frameworks, covering both text-to-3D approaches, such as DreamFusion, GaussianDreamer, SJC, and Magic3D, and image-to-3D approaches, such as Zero-1-to-3 and DreamGaussian, all built on top of the threestudio framework. Setting this up is involved. It requires first installing threestudio itself, then linking this repository's extension into threestudio's custom folder, cloning a couple of additional extensions for the GaussianDreamer baseline, and installing a compatible version of the transformers library to run the detection component. The README states it was tested on Python 3.12 with PyTorch 2.6, CUDA 11.8, and a single 80 GB A100 GPU per training run, so this is meant for people with real GPU infrastructure rather than a personal laptop. The authors also note that a newer, related project called Hallo4D, extending this idea to include time as well as 3D space, has since been released.
Research code from a NeurIPS 2024 paper that plugs into existing text-to-3D and image-to-3D generators to detect and fix inconsistent, contradictory views of a generated object.
Mainly Python. The stack also includes Python, PyTorch, threestudio.
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.