← Back to all news
arXiv AI By Mining Tan, Yinuo Wang, Ziqi Zhou, Weize Quan, Sifei Li, Jingdong Chen, DanDan Zheng, Libin Wang, Weiming Dong

Object-Uni: A Unified Model for Object-Centric Spatial Understanding and Controllable Generation

Read the original on arXiv AI →

Object-Uni is a unified model that integrates pose perception, spatial reasoning, pose-conditioned generation, and novel view synthesis for object-centric spatial understanding and controllable image generation. It treats object pose as an explicit geometric variable shared across tasks and introduces a viewpoint-based orientation abstraction to make pose interpretable by multimodal large language models. The authors also create a new benchmark, UniSpatial-80K, and demonstrate that Object‑Uni improves both pose understanding and pose‑controllable generation compared to existing models.

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 AI.

Related stories

arXiv Machine Learning
Jul 8

PoseVLA: Universal Pose Pretraining for Generalizable Vision-Language-Action Policies

arXiv:2602. 19710v3 Announce Type: replace-cross Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-specific action supervision.

By Haitao Lin, Hanyang Yu, Jingshun Huang, He Zhang, Yonggen Ling, Ping Tan, Xiangyang Xue, Yanwei Fu
nlproboticsmultimodalbenchmarkssafety
More like this →
arXiv Computer Vision
Aug 26

SceneReGen: Generative Reconstruction of 3D Scenes from a Single Image

SceneReGen is a new framework for reconstructing 3D scenes from a single image by generating and assembling complete object meshes within a shared observation‑aligned scene frame. It uses selective pose factorization to encode each object’s observed orientation directly into the generated mesh, while estimating translation and scale from instance‑level and global scene cues. Evaluated on the 3D‑FUTURE dataset, SceneReGen outperforms existing methods on scene‑level metrics and shows strong performance on object‑level metrics, demonstrating its effectiveness in autonomous‑driving and embodied‑AI scenarios.

By Zefan Tian, Yuteng Ye, Yiheng Zhang, Yuhang Yang, Xueqiang Lv, Shizhou Zhang, Le Liu, Di Xu
agentsrobotics
More like this →