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..
DORSal: Diffusion for Object-centric Representations of Scenes $\textit{et al.}$
Authors: Allan Jabri, Sjoerd van Steenkiste, Emiel Hoogeboom, Mehdi S. M. Sajjadi, Thomas Kipf
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate DORSal on challenging synthetic and real-world scenes in three settings: 1) we compare the ability to synthesize novel views of a scene with related approaches, 2) we analyze the capability for simple scene edits: object removal and object transfer between scenes, and 3) we investigate the ability of DORSal to render smooth, view-consistent camera paths. We provide detailed ablations in Appendix C.1. |
| Researcher Affiliation | Collaboration | Allan Jabri , UC Berkeley Sjoerd van Steenkiste Google Research Emiel Hoogeboom Google Deep Mind Mehdi S. M. Sajjadi Google Deep Mind Thomas Kipf Google Deep Mind |
| Pseudocode | No | The paper describes its methods and procedures in narrative text and uses diagrams (e.g., Figure 1, 2) to illustrate architectures, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'We would like to thank Daniel Watson for making the 3Di M codebase readily available for comparison, and help with debugging and onboarding new datasets.' This refers to the codebase of a baseline method (3Di M), not the authors' own code for DORSal. There is no explicit statement or link indicating that the source code for DORSal is being released. |
| Open Datasets | Yes | Multi Shape Net (MSN) (Sajjadi et al., 2022c)... Street View (SV) dataset... Street View imagery and permission for publication have been obtained from the authors (Google, 2007). |
| Dataset Splits | No | The paper mentions training models and evaluating them on a 'test set' (e.g., 'evaluate performance at novel-view synthesis on a test set of 1000 scenes'), but it does not explicitly provide details about a distinct 'validation' dataset split or its size/proportion. |
| Hardware Specification | Yes | We train DORSal on 8 TPU v4 (Jouppi et al., 2023) chips using a batch size of 8 for approx. one week to reach 1M steps. |
| Software Dependencies | No | The paper mentions software components like 'Adam' optimizer, but does not provide specific version numbers for any libraries, frameworks, or other ancillary software dependencies required for replication. |
| Experiment Setup | Yes | We train with a global batch size of 8 and classi๏ฌer-free guidance with a conditioning dropout probability of 0.1 (and an inference guidance weight of 2). We report results after training for 1 000 000 steps... We use a median kernel size of 7 for all edit evaluations (incl. the baselines). |