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benchflow/frontierphysics-material-agnostic-visual-inverse-dynamics
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- **PAC-NeRF data** (`data.zip`, `simulation_data.zip`): Xuan Li, Yi-Ling Qiao, Peter Yichen Chen, Krishna Murthy Jatavallabhula, Ming Lin, Chenfanfu Jiang and Chuang Gan, [*PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification*](https://openreview.net/forum?id=tVkrbkz42vc), ICLR 2023. The [PAC-NeRF code](https://github.com/xuan-li/PAC-NeRF) is MIT-licensed. The authors' Box folder states no licence for the data, and BenchFlow asserts none. The files are copied unchanged, only so that the benchmark can be reproduced. If you are a rights holder and want them removed, open a discussion on this repository. - **Matting checkpoint** (`pytorch_resnet101.pth`): weights from [BackgroundMattingV2](https://github.com/PeterL1n/BackgroundMattingV2) (MIT), copied from the PAC-NeRF Box folder. - **MASIV source** (`masiv/…zip`): [yizhou42/MASIV](https://github.com/yizhou42/MASIV), MIT licence (`LICENSE.md` inside the archive). Zhao et al., *MASIV: Toward Material-Agnostic System Identification from Videos*, arXiv:2508.01112. - **Pretrained Oracle models** (`oracle/pretrained/`): trained by the task author, Yizhou Zhao, with the MASIV code (MIT) and code derived from [Gaussian Splatting](https://github.com/graphdeco-inria/gaussian-splatting), which is under the Inria and MPII GRAPHDECO licence: research and evaluation use only. `oracle/pretrained/LICENSES.md` has the full notices. Browse the [FrontierPhysics task datas
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dataset_licence({ "id": "benchflow/frontierphysics-material-agnostic-visual-inverse-dynamics" })
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stated_licence: "other"
sources: 0