Summary of recent tweets

Justin Johnson has been actively tweeting about his recent research work in the field of AI. He recently presented a paper called "Text2room" at ICCV2023, where he showcased the ability to generate large-scale 3D scenes using text inputs. The paper demonstrates how combining pretrained 2D diffusion models and depth estimators can directly generate meshes of 3D scenes. Justin also shared that the code and models for their ICML 2023 paper, which utilizes hyperbolic geometry to enhance image/text feature learning, are now publicly available.

In another tweet, Justin shares excitement about their ICCV2023 paper "Text2Room," which focuses on generating scene-scale textured 3D meshes from text prompts. He provides links to the project website, code repository, and a video showcasing their work. Additionally, he mentions presenting a paper titled "Hyperbolic Image-Text Representations" at ICML and invites attendees to say hi during the presentation.

Justin's tweets also highlight other projects he has worked on with his colleagues. One such project involves generating animations of people interacting with objects using a diffusion model guided by a learned object interaction field. Another project called Omni3D aims to create a benchmark for monocular 3D detection in wild environments.

Overall, Justin Johnson's recent tweets revolve around his research papers and projects related to generating 3D scenes from text inputs, utilizing hyperbolic geometry for image/text feature learning, and exploring various applications of diffusion models in computer vision tasks.

From analyzing Justin Johnson's tweets, it is evident that he is actively engaged in discussing new trends in AI within the field of computer vision. These trends include generating realistic 3D scenes from text prompts using neural networks and leveraging hyperbolic geometry for improving image/text feature learning.

The sentiment analysis of Justin Johnson's recent tweets suggests a positive outlook on the advancements and progress in AI. He expresses excitement, shares project updates, and invites others to learn more about his work. This indicates that he is optimistic about the direction AI is heading and the potential it holds for solving complex problems in computer vision.