Presentation
QMF-Blend: Quantized Matrix Factorization for Efficient Blendshape Compression
DescriptionIn this paper, we introduce a state-of-the-art blendshape compression algorithm that significantly reduces storage requirements and computational complexity in facial animation. Our approach leverages large sparse matrix factorization and quantization to compress high-dimensional blendshape coefficients into a compact representation, preserving essential features and high-frequency geometric details. The proposed algorithm outperforms existing methods in terms of compression ratio, reconstruction quality, and computational efficiency. We demonstrate its effectiveness through extensive experiments on various animated face models, achieving compression factors of up to 100x over sparse blendshapes with minimal impact on quality. Our technique offers compression rates up to 4.6x better than the prior state-of-the-art while also improving approximation error and preserving features like wrinkles. Additionally, our runtime computation is up to 3x faster than state-of-the-art on CPU and 70% faster than state-of-the-art on GPU, facilitating high-quality facial animation on low-powered computing platforms with limited resources.
Event Type
Technical Papers
TimeTuesday, 16 December 20253:22pm - 3:33pm HKT
LocationMeeting Room S221, Level 2