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..
OBJECT 3DIT: Language-guided 3D-aware Image Editing
Authors: Oscar Michel, Anand Bhattad, Eli VanderBilt, Ranjay Krishna, Aniruddha Kembhavi, Tanmay Gupta
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also introduce 3DIT: single and multi-task models for four editing tasks. Our models show impressive abilities to understand the 3D composition of entire scenes, factoring in surrounding objects, surfaces, lighting conditions, shadows, and physically-plausible object configurations. Training on our new benchmark OBJECT, 3DIT remarkably generalizes to images in the CLEVR dataset as well as the real world. |
| Researcher Affiliation | Collaboration | Oscar Michel1 Anand Bhattad2 Eli Vander Bilt1 Ranjay Krishna1,3 Aniruddha Kembhavi1 Tanmay Gupta1 1Allen Institute for Artificial Intelligence, 2University of Illinois Urbana-Champaign, 3University of Washington |
| Pseudocode | No | The paper describes the model architecture and training process in text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | More information can be found on the project page at https://prior.allenai.org/projects/object-edit. This URL is a project page, not an explicit link to a code repository for the methodology described. |
| Open Datasets | Yes | To promote progress towards this goal, we release OBJECT: a dataset consisting of 400K editing examples created from procedurally generated 3D scenes. |
| Dataset Splits | Yes | We generate 100k training examples for each task, and 1024 scenes for validation and testing. |
| Hardware Specification | Yes | This batch size is achieved by using a local batch size of 64 across 40GB NVIDIA RTX A6000 GPUs, along with two gradient accumulation steps. |
| Software Dependencies | No | The paper mentions using Adam W optimizer and building upon Stable Diffusion and Zero-1-to-3, but does not provide specific version numbers for software dependencies such as Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | Our approach uses an effective batch size of 1024... We train on images with a resolution of 256 256... We utilize the Adam W optimizer, with a learning rate of 1e-4 for all parameters of the model except for those of the concatenation MLP, which uses a learning rate of 1e 3. Our training process runs for a total of 20,000 steps... For inference, we generate images with the DDIM [68] sampler using 200 steps. We do not use classifier-free guidance, i.e. the cfg term is set to 1.0. |