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arXiv Computer Vision By Zhenggang Tang, Yuehao Wang, Yuchen Fan, Jun-Kun Chen, Yu-Ying Yeh, Kihyuk Sohn, Zhangyang Wang, Qixing Huang, Alexander Schwing, Rakesh Ranjan, Dilin Wang, Zhicheng Yan

Autoregressive Diffusion for Compositional Text-to-Scene Generation

Read the original on arXiv Computer Vision →

The paper introduces a new sequential text‑to‑scene generation framework that jointly produces scene layouts and objects. It proposes a 3D Autoregressive Diffusion model (3D‑ARD+) that first generates coarse 3D latents conditioned on text and the current scene, then refines them into fine‑grained object geometry and appearance. A large dataset of 230K indoor scenes with paired text instructions is curated for training, and the model demonstrates the ability to place objects according to complex spatial and semantic text prompts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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