| --- |
| language: |
| - en |
| base_model: |
| - Yuppie1204/NeoVerse |
| tags: |
| - cornerbench |
| - video-generation |
| - multi-view |
| - autonomous-driving |
| - control-branch |
| --- |
| |
| # CornerFactory: NeoObj, NeoView, and NeoEmb |
|
|
| This repository groups the three task-specific control branches used by CornerFactory. The release uses the paper names **NeoObj**, **NeoView**, and **NeoEmb**, with one subdirectory per module. |
|
|
| This release provides the selected **1280 × 720** control-branch weights. They are not the complete NeoVerse backbone, and they are not interchangeable with a standalone Diffusers pipeline checkpoint. |
|
|
| | Module | Task | File | |
| | --- | --- | --- | |
| | NeoObj | Masked cross-view object completion | `NeoObj/model.safetensors` | |
| | NeoView | Perturbed-pose novel-view completion | `NeoView/model.safetensors` | |
| | NeoEmb | Vehicle-configuration transfer | `NeoEmb/model.safetensors` | |
|
|
| Each file contains 240 BF16 tensors, with 3,049,216,192 parameters in the control branch (approximately 6.10 GB per module). All three training configurations use 720 × 1280 images and 81-frame clips. This does not mean that every released example has 81 frames. |
|
|
| ## Download |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| checkpoint = hf_hub_download( |
| repo_id="cornerbench-anon-2027/CornerFactory", |
| filename="NeoObj/model.safetensors", # or NeoView / NeoEmb |
| ) |
| ``` |
|
|
| During private review, the account that owns the repository must authenticate before downloading. Once public, downloading these files will not require an application or access request. |
|
|
| ## Loading the control branch |
|
|
| The matching CornerFactory/NeoVerse pipeline must already be initialized with its backbone, text encoder, tokenizer, VAE, and a compatible controller architecture. The files in this repository replace the controller parameters only. |
|
|
| ```python |
| from load_control_branch import load_control_branch |
| |
| # `pipe` is an already-initialized compatible pipeline. |
| load_control_branch(pipe.control_branch, checkpoint) |
| ``` |
|
|
| `load_control_branch.py` checks all parameter names and shapes and loads strictly. It does not reconstruct the complete inference pipeline. Task-specific camera calibration, conditioning, preprocessing, scheduler settings, and any inference acceleration LoRA must match the accompanying implementation. End-to-end inference code is a separate release artifact. |
|
|
| ## Evaluation scope |
|
|
| These 720p weights are provided for inspection and subsequent reproduction. Numerical results depend on the corresponding evaluation configuration; this release does not claim that these weights reproduce every result in the paper. |
|
|
| ## Integrity |
|
|
| See `manifest.json`, each module's `release_config.json`, and `SHA256SUMS`. All checkpoint tensors were checked for finite values, and the release files match the source checkpoint hashes. This integrity check is not an end-to-end inference benchmark. |
|
|
| ## Related data |
|
|
| The separate [CornerBench-1200 dataset](https://huggingface.co/datasets/cornerbench-anon-2027/CornerBench-1200) contains three-view generated driving cases. These generated observations are research artifacts and do not establish real-world driving safety. |
|
|
