65 citations · 71 across the 5 of their papers we have counts for
5 papers
Vid3D: Synthesis of Dynamic 3D Scenes using 2D Video Diffusion
Rishab Parthasarathy, Zachary Ankner, Aaron Gokaslan
A recent frontier in computer vision has been the task of 3D video generation, which consists of generating a time-varying 3D representation of a scene. To generate dynamic 3D scen…
Self-Directed Synthetic Dialogues and Revisions Technical Report
Nathan Lambert, Hailey Schoelkopf, Aaron Gokaslan +3
Synthetic data has become an important tool in the fine-tuning of language models to follow instructions and solve complex problems. Nevertheless, the majority of open data to date…
On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
Daniel McDuff, Tim Korjakow, Scott Cambo +9
Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use c…
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
Aaron Gokaslan, A. Feder Cooper, Jasmine Collins +6
We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2…
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Hugo Laurençon, Lucile Saulnier, Thomas Wang +51
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop,…