.title-container { display: flex; flex-direction: column; /* Stack elements vertically */ justify-content: center; align-items: center; } .title { font-size: 2em; text-align: center; color: #333; font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0 0.2em; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #6495ED, #4169E1); /* Blue gradient */ -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .subheading { font-size: 1.5em; /* Adjust the size as needed */ text-align: center; color: #555; /* Adjust the color as needed */ font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ } .authors { font-size: 1em; /* Adjust the size as needed */ text-align: center; color: #777; /* Adjust the color as needed */ font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ padding-top: 1em; } .affil { font-size: 1em; /* Adjust the size as needed */ text-align: center; color: #777; /* Adjust the color as needed */ font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ }

Magiv3 is a unified vision-language model for comic understanding. Given an image and a task prompt, it autoregressively produces text-only outputs for a range of tasks: localising panels, characters, texts and speech-bubble tails, performing OCR, and grounding characters referenced in a caption to their locations in the panel.

Usage

model = AutoModelForCausalLM.from_pretrained("ragavsachdeva/magiv3", torch_dtype=torch.float16, trust_remote_code=True).cuda().eval()
processor = AutoProcessor.from_pretrained("ragavsachdeva/magiv3", trust_remote_code=True)

model.predict_detections_and_associations(images, processor)
model.predict_ocr(images, processor)
model.predict_character_grounding(images, captions, processor)

License and Citation

The provided model and datasets are available for unrestricted use in personal, research, non-commercial, and not-for-profit endeavors. For any other usage scenarios, kindly contact me via email, providing a detailed description of your requirements, to establish a tailored licensing arrangement. My contact information can be found on my website: ragavsachdeva [dot] github [dot] io

@InProceedings{Sachdeva25,
      title={From Panels to Prose: Generating Literary Narratives from Comics},
      author={Ragav Sachdeva and Andrew Zisserman},
      booktitle={IEEE International Conference on Computer Vision (ICCV)},
      year={2025},
      eprint={2503.23344},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.23344}
}