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..
3D-Aware Scene Manipulation via Inverse Graphics
Authors: Shunyu Yao, Tzu Ming Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, Bill Freeman, Josh Tenenbaum
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our editing scheme based on 3D-SDN is superior to its 2D counterpart. |
| Researcher Affiliation | Collaboration | Shunyu Yao IIIS, Tsinghua University Tzu-Ming Harry Hsu MIT CSAIL Jun-Yan Zhu MIT CSAIL Jiajun Wu MIT CSAIL Antonio Torralba MIT CSAIL William T. Freeman MIT CSAIL, Google Research Joshua B. Tenenbaum MIT CSAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Please check out our code and website for more details. ... The code and full results can be found at our website. |
| Open Datasets | Yes | We conduct experiments on two street scene datasets: Virtual KITTI [Gaidon et al., 2016] and Cityscapes [Cordts et al., 2016]. |
| Dataset Splits | No | The paper specifies training and testing splits but does not explicitly mention a separate validation split for reproducing experiments. It states: 'For each world, we use either the first or the last 80% consecutive frames for training and the rest for testing.' |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions software tools like Mask-RCNN and VGG network but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We empirically set λreproj = 0.1. We first train the network with Lpred using Adam [Kingma and Ba, 2015] with a learning rate of 10^-3 for 256 epochs and then fine-tune the model with Lpred + λreproj Lreproj and REINFORCE with a learning rate of 10^-4 for another 64 epochs. ... We set λFM = 5 and λRecon = 10, and train the textural branch for 60 epochs on Virtual KITTI and 100 epochs on Cityscapes. |