ColView: Consistent Text-Guided Grayscale Scene Colorization From Multi-View Images

The colorization of scenes from multi-view grayscale images plays a crucial role in applications such as augmented reality and virtual exhibitions. Existing methods combine NeRF with an automatic colorization model, averaging multiple colorized patches to reduce inconsistency. However, they still face three key limitations: (1) Current methods cannot produce diverse colorization results due to the lack of multimodal conditional inputs, (2) They struggle to maintain multi-view consistency caused by unreliable geometric correspondence and ineffective propagation mechanisms, and (3) Computational inefficiency from NeRF's dense ray sampling and numerical integration. In this paper, we propose ColView, a unified framework for text-guided grayscale scene colorization that achieves both automatic and controllable colorization of grayscale scenes from multi-view grayscale images. First, for flexible color control, we leverage text description as the input to guide the colorization process, which allows users to specify desired colors through natural language descriptions. Second, to ensure multi-view consistency, we introduce a multi-view consistent colorization module that explicitly models dependencies between different views. This module follows three key steps: cross-view attention mechanism for collaborative key-view colorization, feature matching for inter-view correspondence establishment, and correspondence-guided feature propagation. Third, to improve computational efficiency, we adopt 3D Gaussian Splatting as our underlying representation. This explicit point-based representation renders significantly faster than NeRF. Extensive experimental results demonstrate that our method achieves superior visual quality and computational efficiency. Our code and models are publicly available athttps://github.com/ChchNiu/ColView.