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

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.

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 contains three-view generated driving cases. These generated observations are research artifacts and do not establish real-world driving safety.