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..
InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint
Authors: Zhenzhi Wang, Jingbo Wang, Yixuan Li, Dahua Lin, Bo Dai
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results highlight the capability of our framework to generate interactions with multiple human characters and its potential to work with off-the-shelf physics-based character simulators. Code is available at https://github.com/zhenzhiwang/intercontrol. Extensive experiments in Human ML3D [14] and KIT-ML [47] datasets quantitatively validates our joint control ability, and the user study on generated interactions shows a clear preference over previous methods. |
| Researcher Affiliation | Collaboration | Zhenzhi Wang1, Jingbo Wang2, Yixuan Li1, Dahua Lin1,2, Bo Dai3,2 1The Chinese University of Hong Kong, 2Shanghai Artificial Intelligence Laboratory, 3The University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 Two-people interaction model inference |
| Open Source Code | Yes | Code is available at https://github.com/zhenzhiwang/intercontrol. |
| Open Datasets | Yes | Datasets. We conduct experiments on Human ML3D [14] and KIT-ML [47] following MDM [55]. |
| Dataset Splits | Yes | Datasets. We conduct experiments on Human ML3D [14] and KIT-ML [47] following MDM [55]. |
| Hardware Specification | Yes | Inference time analysis on a NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions 'Python scripts' and 'Py Torch-like code' but does not specify their version numbers or the versions of other major libraries like PyTorch itself. It refers to specific models/optimizers by their original paper citations (e.g., Adam W [39], CLIP [48], GPT-4 [43], L-BFGS [37]), but these are not software dependency versions in the typical sense. |
| Experiment Setup | Yes | We run L-BFGS [37] in IK guidance 5 times for the first 990 denoising steps and 10 times for the last 10 denoising steps on the posterior mean µt; and once for the first 990 steps and 10 times for the last 10 steps on clean motion x0. We use Adam W [39] optimizer and set the learning rate as 1e-5. |