megvii-research/IJCAI2023-CoNR
A deep learning system that synthesizes animated dance videos from hand-drawn anime character sheets using collaborative neural rendering.
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The project implements a collaborative neural rendering pipeline that takes hand-drawn anime character sheets as input and outputs vivid dancing videos. It uses deep learning techniques and PyTorch to enable the neural rendering of anime-style content from static 2D character illustrations. The system was developed by Megvii Research and accepted to the IJCAI 2023 Special Track on AI, the Arts, and Creativity.
Frequently asked
- What is megvii-research/IJCAI2023-CoNR?
- A deep learning system that synthesizes animated dance videos from hand-drawn anime character sheets using collaborative neural rendering.
- Is IJCAI2023-CoNR open source?
- Yes — megvii-research/IJCAI2023-CoNR is open source, released under the MIT license.
- What language is IJCAI2023-CoNR written in?
- megvii-research/IJCAI2023-CoNR is primarily written in Jupyter Notebook.
- How popular is IJCAI2023-CoNR?
- megvii-research/IJCAI2023-CoNR has 805 stars on GitHub.
- Where can I find IJCAI2023-CoNR?
- megvii-research/IJCAI2023-CoNR is on GitHub at https://github.com/megvii-research/IJCAI2023-CoNR.