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..
SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects
Authors: Jiayi Liu, Denys Iliash, Angel Chang, Manolis Savva, Ali Mahdavi Amiri
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our method outperforms the state-of-the-art in articulated object creation by a large margin in terms of the generated object realism, resemblance to the input image, and reconstruction quality. [...] 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Jiayi Liu1, Denys Iliash1, Angel X. Chang1,2, Manolis Savva1, Ali Mahdavi-Amiri1 1Simon Fraser University, 2Canada-CIFAR AI Chair, Amii |
| Pseudocode | No | The paper describes the pipeline and model architecture in text and diagrams (Figures 1, 2, 3, 6) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | 3dlg-hcvc.github.io/singapo |
| Open Datasets | Yes | We collect data from Part Net-Mobility dataset (Xiang et al., 2020) to train our model across 7 categories [...] We also use 135 objects from the ACD dataset (Iliash et al., 2024) for additional evaluation in the zero-shot manner to test the generalization capability. |
| Dataset Splits | Yes | With several augmentation strategies applied, we end up with 3,063 objects paired with 20 images rendered at resting state for training, and additional 77 objects paired with 2 random views for testing. We also use 135 objects from the ACD dataset (Iliash et al., 2024) for additional evaluation in the zero-shot manner to test the generalization capability. In total, we have 55K training samples and 424 test samples in the experiments. |
| Hardware Specification | Yes | Our model is trained on 4 NVIDIA A40 GPUs for 23 hours. |
| Software Dependencies | No | The paper mentions using the Adam W optimizer but does not specify version numbers for any key software components like Python, PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | Yes | We train our model for 200 epochs after initializing CAGE pretrained weights with a batch size of 40 and each with 16 timesteps sampled from the diffusion process for each iteration. We train 1,000 diffusion steps in total. We use the Adam W Loshchilov (2017) optimizer with learning rate 5e 4 for ICA module and 5e 5 for the base model parameters, and the beta values are set to (0.9, 0.99). We schedule the learning rate with 3 epochs of warm-up from 1e 6 to the initial learning rate and then consine annealing to 1e 5. The network has 6 layers of attention blocks with 4 heads and 128 hidden units. |