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..
Semantic-Aware Human Object Interaction Image Generation
Authors: Zhu Xu, Qingchao Chen, Yuxin Peng, Yang Liu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our method significantly improves generation quality under both HOI-specific and conventional image evaluation metrics. |
| Researcher Affiliation | Academia | 1Wangxuan Institute of Computer Technology, Peking University 2National Institute of Health Data Science, Peking University. |
| Pseudocode | Yes | The pseudo code of our pose and interaction boundary guided sampling is shown in Algorithm 1. The pipeline is shown in Algorithm 2. |
| Open Source Code | Yes | The code is available at https://github.com/XZPKU/SA-HOI.git |
| Open Datasets | Yes | Our dataset consists of 150 HOI categoires, covering human-object, human-animal, and human-human interaction scenarios for comprehensive evaluation. The categories are all collected from public HOI detection data-set HICO-DET (Chao et al., 2015). |
| Dataset Splits | No | The paper describes using a pre-trained model (Stable Diffusion v1.5) and a dataset for evaluation, but does not specify explicit train/validation splits for its own experimental setup or model training. |
| Hardware Specification | Yes | For the experiments, we use two A100 80G GPUs to sample images from the pre-trained models Stable Diffusion v1.5 (Rombach et al., 2022). |
| Software Dependencies | No | The paper mentions specific models and toolboxes used (e.g., Stable Diffusion v1.5, RTMPose toolbox, Mask-RCNN) but does not provide specific version numbers for underlying software dependencies like programming languages, frameworks (e.g., Python, PyTorch, TensorFlow), or other libraries. |
| Experiment Setup | Yes | For CFG, we adopt guidance scale of 7.5, the text prompt is A photo of a person verbing a/an object. for HOI category <verb, object>, and the negative prompt is set as . We adopt DDIMScheduler(von Platen et al., 2022) with 50 steps for the denoising process, and all the generated images are with size 512 512. Hyperparameters θ, δ, ϕ0, α, T are set as 0.01, 1, 1, 0.6 and 4. |