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kyutai/
ovie

Name: ovie
Availability: InStock

pytorch_model_hub_mixin

2603.23488

mit

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OVIE — One View Is Enough!

Monocular Training for In-the-Wild Novel View Generation

OVIE is a novel view synthesis model that generates a new viewpoint of a scene from a single image and a target camera pose. Unlike most prior work, it is trained entirely on unpaired in-the-wild images — no multi-view supervision required.

Model architecture

OVIE is a convolutional encoder–decoder with a Vision Transformer (ViT) bottleneck conditioned on camera parameters via adaptive layer normalisation (AdaLN):

Encoder: cascaded downsampling ConvBlocks (3 scales)
Bottleneck: 12-layer ViT (hidden size 768, 12 heads) with AdaLN camera conditioning
Decoder: cascaded upsampling ConvBlocks (3 scales)
Camera conditioning: a 7-dimensional pose encoding (rotation + translation) projected into the ViT hidden space
Parameters: ~143M

Usage

python

import torch
from models.models import OVIEModel
from utils.pose_enc import extri_intri_to_pose_encoding
from torchvision.transforms import ToTensor
from PIL import Image

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load model
model = OVIEModel.from_pretrained("kyutai/ovie", revision="v1.0").to(device)
model.eval()
image_size = model.image_size  # 256

# Prepare input image
img_pil = Image.open("image.jpg").convert("RGB").resize((image_size, image_size))
img_tensor = ToTensor()(img_pil).unsqueeze(0).to(device)

# Define target camera pose (3x4 extrinsics)
extrinsics = torch.tensor([[[1.0, 0.0, 0.0, -1.25],
                            [0.0, 1.0, 0.0,  0.5],
                            [0.0, 0.0, 1.0, -2.0]]], device=device)
dummy_intrinsics = torch.zeros(1, 1, 3, 3, device=device)

camera = extri_intri_to_pose_encoding(
    extrinsics=extrinsics.unsqueeze(0),
    intrinsics=dummy_intrinsics,
    image_size_hw=(image_size, image_size),
)
cam_token = camera[..., :7].squeeze(0)

# Generate novel view
with torch.no_grad():
    pred = model(x=img_tensor, cam_params=cam_token)
# pred: (1, 3, 256, 256) tensor in [0, 1]

See the repository for full installation instructions and example notebooks:

inference_huggingface.ipynb — loads directly from this Hub page
inference_local.ipynb — loads from a local checkpoint

Training

OVIE is trained on a diverse mix of in-the-wild internet images (ImageNet, Places365, OSV5M, OpenImages) with no multi-view pairs. Training uses a combination of L2 reconstruction loss, LPIPS perceptual loss, and an adversarial loss with a DINO-based discriminator. Camera poses are sampled synthetically from a distribution of plausible viewpoint changes.

Evaluation

The model is evaluated on DL3DV and Real Estate 10K (RE10K) using PSNR, SSIM, and LPIPS. See the paper for full quantitative results.

Citation

bibtex

@misc{ovie2026,
      title={One View Is Enough! Monocular Training for In-the-Wild Novel View Generation},
      author={Adrien Ramanana Rahary and Nicolas Dufour and Patrick Perez and David Picard},
      year={2026},
      eprint={2603.23488},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.23488},
}