Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ContactGen: Contact-Guided Interactive 3D Human Generation for Partners
Authors: Dongjun Gu, Jaehyeok Shim, Jaehoon Jang, Changwoo Kang, Kyungdon Joo
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate Contact Gen on the CHI3D dataset, where our method generates physically plausible and diverse poses compared to comparison methods. |
| Researcher Affiliation | Academia | Dongjun Gu, Jaehyeok Shim, Jaehoon Jang, Changwoo Kang, Kyungdon Joo* Artifcial Intelligence Graduate School, UNIST EMAIL |
| Pseudocode | Yes | Algorithm 1: Training; Algorithm 2: Guided Sampling |
| Open Source Code | Yes | Source code is available at https://dongjunku.github.io/contactgen. |
| Open Datasets | Yes | We basically use CHI3D (Fieraru et al. 2020), which is a 3D motion capture dataset of 8 close human interaction scenarios |
| Dataset Splits | No | The paper mentions using the CHI3D dataset and performing pre-processing for training but does not provide specific details on train/validation/test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions support from an 'HPC Support Project' but does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions using SMPL-X representation, Adam optimizer, DDM, and classifier-free guidance, but it does not specify version numbers for any software libraries or frameworks used. |
| Experiment Setup | Yes | For the training procedure, noise is sampled from the linear noise scheduler initialized with β0 = 5e 6, βT = 5e 3, and diffusion step T = 1000. |