Thinking with Camera: A Unified Multimodal Model for Camera-Centric
  Understanding and Generation
Authors: name: Kang Liao, name: Size Wu, name: Zhonghua Wu, name: Linyi Jin, name: Chao Wang, name: Yikai Wang, name: Fei Wang, name: Wei Li, name: Chen Change Loy
Organization: MMLab@NTU

Camera-centric understanding and generation are two cornerstones of spatial
intelligence, yet they are typically studied in isolation. We present Puffin, a
unified camera-centric multimodal model that extends spatial awareness along
the camera dimension. Puffin integrates language regression and diffusion-based
generation to interpret and create scenes from arbitrary viewpoints. To bridge
the modality gap between cameras and vision-language, we introduce a novel
paradigm that treats camera as language, enabling thinking with camera. This
guides the model to align spatially grounded visual cues with photographic
terminology while reasoning across geometric context. Puffin is trained on
Puffin-4M, a large-scale dataset of 4 million vision-language-camera triplets.
We incorporate both global camera parameters and pixel-wise camera maps,
yielding flexible and reliable spatial generation. Experiments demonstrate
Puffin superior performance over specialized models for camera-centric
generation and understanding. With instruction tuning, Puffin generalizes to
diverse cross-view tasks such as spatial imagination, world exploration, and
photography guidance. We will release the code, models, dataset pipeline, and
benchmark to advance multimodal spatial intelligence research.