Maoyuan Xu
Chengdu, Sichuan, China.
As an R&D Scientist at Ubisoft La Forge, I specialize in Machine Learning, Computer Vision, and Computer Graphics, with a strong focus on advancing the frontiers of game asset creation. My work spans low-level image processing, high-level vision tasks, and the application of diffusion models, all aimed at developing innovative technologies for asset generation and processing.
I expertise in AI-Generated Content (AIGC) and High-fidelity Asset Processing to achieve production-ready quality contents. My interests lie in applied research: identifying real-world production challenges, designing novel solutions, and deploying these innovations into practical tools and workflows to enable more refined and controllable content generation.
In addition to my research, I have the privilege of mentoring research students during their internships, providing hands-on guidance in those rapidly advancing fields. We are consistently looking to forge collaborations with motivated Master’s and PhD candidates who are passionate about pushing the boundaries of knowledge and contributing to novel solutions in Generative AI and Computer Vision.
news
| Sep 12, 2025 | Our paper, titled Chord: Chain of Rendering Decomposition for PBR Material Estimation from Generated Texture Images, which proposes a novel two-stage generate-and-estimate framework for PBR material generation, has been officially accepted to SIGGRAPH Asia 2025. The arXiv version is available HERE. |
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| Aug 20, 2025 | Our new paper for PBR material super-resolution has been submitted to AAAI-2026. |
| Jul 08, 2021 | Our paper, entitled NFCNN: Toward a Noise Fusion Convolutional Neural Network for Image Denoising, proposes a Noise Fusion Convolutional Neural Network (NFCNN) designed for image denoising. Compared to existing methods, NFCNN demonstrates a notable advantage in preserving texture details. This work has been accepted for publication in Signal, Image and Video Processing, a journal published by Springer. The offcial full paper is available HERE. |
selected publications
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NFCNN: Toward a Noise Fusion Convolutional Neural Network for Image DenoisingIn Signal, Image and Video Processing, 2021