zonszer/Gencine_ckpts

Generative Cinematographer

Composing Camera and Object Motion in 3D

Project website · Paper · Code

Inference checkpoint for gencine, a geometry-conditioned video generation model. The provided examples generate 21 RGB frames at 832×480 with 50 denoising steps, CFG 3, sigma shift 5, BF16 precision and untiled decoding.

Files

• model.json: model configuration and SHA-256 checksums.
• dit.safetensors: diffusion transformer.
• vace.safetensors: geometry-conditioning model.
• scale3d.safetensors: scale-conditioning model.
• vae_decoder.safetensors: video decoder.

Download

Install the Hugging Face CLI with pip install huggingface_hub, then download the public checkpoint bundle:

hf download zonszer/Gencine_ckpts --local-dir weights/gencine

Inference

Install the gencine runtime following its project README. From the project root, run a provided example:

python -m gencine --weights weights/gencine \
  --manifest examples/manifest.json --example hike --output outputs/hike_21

The runtime verifies weight checksums from model.json and runs offline with preprocessed inputs. The examples and environment instructions are provided by the gencine code repository.

See LICENSE and THIRD_PARTY_NOTICES.md for licensing.

Citation

If you use this project, please cite:

@misc{zhang2026generativecinematographer,
  title         = {Generative Cinematographer: Composing Camera and Object Motion in 3D},
  author        = {Zhang, Jiahan and Yang, Chaohao and Guruprasad, Namitha and Banerjee, Vivekjyoti
                   and Nguyen, Trong-Tung and Yuille, Alan and Bhattad, Anand},
  year          = {2026},
  eprint        = {2610.02180},
  archivePrefix = {arXiv}
}