1 citations · 1 across the 13 of their papers we have counts for
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HOLODECK 2.0: Vision-Language-Guided 3D World Generation with Editing
Zixuan Bian, Ruohan Ren, Yue Yang +1
3D scene generation plays a crucial role in gaming, artistic creation, virtual reality, and many other domains. However, current 3D scene design still relies heavily on extensive m…
DenseAnnotate: Enabling Scalable Dense Caption Collection for Images and 3D Scenes via Spoken Descriptions
Xiaoyu Lin, Aniket Ghorpade, Hansheng Zhu +9
With the rapid adoption of multimodal large language models (MLLMs) across diverse applications, there is a pressing need for task-centered, high-quality training data. A key limit…
Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation
Yue Yang, Ajay Patel, Matt Deitke +8
Reasoning about images with rich text, such as charts and documents, is a critical application of vision-language models (VLMs). However, VLMs often struggle in these domains due t…
Concept Lancet: Image Editing with Compositional Representation Transplant
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan +3
Diffusion models are widely used for image editing tasks. Existing editing methods often design a representation manipulation procedure by curating an edit direction in the text em…
ViUniT: Visual Unit Tests for More Robust Visual Programming
Artemis Panagopoulou, Honglu Zhou, Silvio Savarese +4
Programming based approaches to reasoning tasks have substantially expanded the types of questions models can answer about visual scenes. Yet on benchmark visual reasoning data, wh…
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
Matt Deitke, Christopher Clark, Sangho Lee +47
Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good perfor…