Reproduce “Object-Uni: A Unified Model for Object-Centric Spatial Understanding and Controllable Generation” on OpenResearch
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About this paper
Mining Tan, Yinuo Wang, Ziqi Zhou, Weize Quan, Sifei Li, Jingdong Chen, DanDan Zheng, Libin Wang and 1 more
Object-Uni integrates object-centric spatial understanding and controllable generation into a single framework, allowing multimodal large language models to both perceive and synthesize continuous object poses. It introduces a viewpoint-based orientation abstraction and the UniSpatial-80K dataset, demonstrating improved object orientation estimation and enhanced pose-controllable image generation with better spatial grounding and semantic fidelity compared to baselines.
Key insights
- Formulating object-centric spatial intelligence as a unified understanding-and-generation problem allows for a more coherent flow of spatial information and geometrically consistent visual AI systems.
- Bridging continuous geometric variables with an MLLM's language-level reasoning via viewpoint abstraction enables effective manipulation of object poses through natural language.
- An object-token-grounded pose anchor mechanism significantly improves the stability and correct attribution of pose predictions to specific object instances, especially in multi-object scenes.
Summary by alphaXiv.
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