3 accepted papers
We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision–language models with physics-based simulation to produce physically plausible, simulation-ready, and intersection-free 3D scenes. Given a text prompt, PAT3D generates 3D objects, infers their…
Diffusion models have emerged as a powerful generative method, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack explicit control over the 3D structure in the generated images. Consequently, this hinders our ability to obtain detailed…
"Knowing merely where the target is located is not sufficient for many real-life scenarios. In contrast, capturing rich details about the tracked target via its semantic trajectory, i.e. who/what this target is interacting with and when, where, and how they are interacting over time, is especially c…