Presentation

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AssetDropper: Asset Extraction via Diffusion Models with Reward-Driven Optimization
SessionLinear & Non-Linear AI Designs
DescriptionAssetDropper is a novel framework for extracting standardized assets from reference images, addressing challenges such as occlusion and distortion. Leveraging both synthetic and real-world datasets, along with a reward-driven feedback mechanism, it achieves state-of-the-art performance in asset extraction and provides designers with a versatile open-world asset palette.
Authors
Lanjiong Li
The Hong Kong University of Science and Technology (Guangzhou)
Guanhua Zhao
School of Electronic and Computer Engineering, Peking University
Lingting Zhu
The University of Hong Kong
Zeyu Cai
The Hong Kong University of Science and Technology (Guangzhou)
Lequan Yu
The University of Hong Kong
Jian Zhang
School of Electronic and Computer Engineering, Peking University
Zeyu Wang
The Hong Kong University of Science and Technology (Guangzhou)
The Hong Kong University of Science and Technology
Event Type
Technical Paper
TimeMonday, 11 August 20259:00am - 9:10am PDT
LocationWest Building, Rooms 211-214
ACM Digital Library Journal Papers' PDFs
Conference Papers' PDFs
Session Time & Location
Sunday, 10 August 20256:00pm - 8:45pm PDTWest Building, Ballroom AB
Monday, 11 August 20259:00am - 10:30am PDTWest Building, Rooms 211-214
Recordings
Livestreamed
Recorded
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