ARC Lab, Tencent PCG

company

Verified

AI & ML interests

ARC mainly focuses on areas of computer vision, speech, and natural language processing, including speech/video generation, enhancement, retrieval, understanding, AutoML, etc. Considering research developments and industry trends, ARC consistently pursues exploration, innovation, and breakthroughs in technologies.

Recent Activity

bluestyle97 updated a Space about 3 hours ago

TencentARC/FreeSplatter

yshan2u authored a paper about 23 hours ago

Cobra: Efficient Line Art COlorization with BRoAder References

juxuan27 authored a paper about 23 hours ago

Cobra: Efficient Line Art COlorization with BRoAder References

View all activity

TencentARC's activity

bluestyle97

updated a Space about 3 hours ago

FreeSplatter

🔥

Reconstruct 3D Gaussians from unposes images.

yshan2u

authored a paper about 23 hours ago

Cobra: Efficient Line Art COlorization with BRoAder References

Paper • 2504.12240 • Published 1 day ago • 19

juxuan27

authored a paper about 23 hours ago

Cobra: Efficient Line Art COlorization with BRoAder References

Paper • 2504.12240 • Published 1 day ago • 19

l-li

authored a paper about 23 hours ago

Cobra: Efficient Line Art COlorization with BRoAder References

Paper • 2504.12240 • Published 1 day ago • 19

JunhaoZhuang

authored a paper about 23 hours ago

Cobra: Efficient Line Art COlorization with BRoAder References

Paper • 2504.12240 • Published 1 day ago • 19

JunhaoZhuang

posted an update 1 day ago

Post

1597

We are excited to announce the release of our paper, "Cobra: Efficient Line Art COlorization with BRoAder References," along with the official code! Cobra is a novel efficient long-context fine-grained ID preservation framework for line art colorization, achieving high precision, efficiency, and flexible usability for comic colorization. By effectively integrating extensive contextual references, it transforms black-and-white line art into vibrant illustrations.

We invite you to explore Cobra and share your feedback! You can access the paper and code via the following links: [PDF](https://arxiv.org/abs/2504.12240) and [Project page](https://zhuang2002.github.io/Cobra/). We eagerly anticipate your engagement and support!

Thank you for your interest!