L-C4: Language-Based Video Colorization for Creative and Consistent Color

• URL: http://arxiv.org/abs/2410.04972v2
• Date: Sun, 3 Nov 2024 09:27:15 GMT
• Title: L-C4: Language-Based Video Colorization for Creative and Consistent Color
• Authors: Zheng Chang, Shuchen Weng, Huan Ouyang, Yu Li, Si Li, Boxin Shi,
• Abstract summary: We present Language-based video colorization for Creative and Consistent Colors (L-C4) Our model is built upon a pre-trained cross-modality generative model. We propose temporally deformable attention to prevent flickering or color shifts, and cross-clip fusion to maintain long-term color consistency.
• Score: 59.069498113050436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic video colorization is inherently an ill-posed problem because each monochrome frame has multiple optional color candidates. Previous exemplar-based video colorization methods restrict the user's imagination due to the elaborate retrieval process. Alternatively, conditional image colorization methods combined with post-processing algorithms still struggle to maintain temporal consistency. To address these issues, we present Language-based video Colorization for Creative and Consistent Colors (L-C4) to guide the colorization process using user-provided language descriptions. Our model is built upon a pre-trained cross-modality generative model, leveraging its comprehensive language understanding and robust color representation abilities. We introduce the cross-modality pre-fusion module to generate instance-aware text embeddings, enabling the application of creative colors. Additionally, we propose temporally deformable attention to prevent flickering or color shifts, and cross-clip fusion to maintain long-term color consistency. Extensive experimental results demonstrate that L-C4 outperforms relevant methods, achieving semantically accurate colors, unrestricted creative correspondence, and temporally robust consistency.

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