6 papers
Tail-Aware Post-Training Quantization for 3D Geometry Models
Sicheng Pan, Chen Tang, Shuzhao Xie +6
The burgeoning complexity and scale of 3D geometry models pose significant challenges for deployment on resource-constrained platforms. While Post-Training Quantization (PTQ) enabl…
Accelerating Parallel Diffusion Model Serving with Residual Compression
Jiajun Luo, Yicheng Xiao, Jianru Xu +5
Diffusion models produce realistic images and videos but require substantial computational resources, necessitating multi-accelerator parallelism for real-time deployment. However,…
EVOS: Efficient Implicit Neural Training via EVOlutionary Selector
Weixiang Zhang, Shuzhao Xie, Chengwei Ren +5
We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all sampl…
SizeGS: Size-aware Compression of 3D Gaussian Splatting via Mixed Integer Programming
Shuzhao Xie, Jiahang Liu, Weixiang Zhang +7
Recent advances in 3D Gaussian Splatting (3DGS) have greatly improved 3D reconstruction. However, its substantial data size poses a significant challenge for transmission and stora…
Staleness-Centric Optimizations for Parallel Diffusion MoE Inference
Jiajun Luo, Lizhuo Luo, Jianru Xu +4
Mixture-of-Experts-based (MoE-based) diffusion models demonstrate remarkable scalability in high-fidelity image generation, yet their reliance on expert parallelism introduces crit…
MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation
Shuzhao Xie, Weixiang Zhang, Chen Tang +4
3D Gaussian Splatting demonstrates excellent quality and speed in novel view synthesis. Nevertheless, the huge file size of the 3D Gaussians presents challenges for transmission an…