1 citations · 3 across the 13 of their papers we have counts for
13 papers
Odin: Primitive-Level Synchronization for Distributed Point-Based Neural Rendering
Zhenxiang Ma, Zeyu He, Yuanzhen Zhou +6
Point-based neural rendering (PBNR) represents 3D scenes as explicit, trainable primitives and underpins high-quality reconstruction and emerging embodied AI and world-model pipeli…
DiTango: Cost-Effective Parallel Diffusion Generation with Selective Attention State Reuse
Yuyang Chen, Runxin Zhong, Zan Zong +3
Recent advances in AI-generated content have driven widespread adoption of Diffusion Transformers (DiTs) for high-resolution, long-duration content generation. While parallelizatio…
AdaPonderLM: Gated Pondering Language Models with Token-Wise Adaptive Depth
Shixiang Song, He Li, Zitong Wang +6
Test-time scaling via recurrent/iterative Transformers enables large language models to spend more computation at inference, but most pretrained recurrent LMs run a fixed number of…
PonderLM-3: Adaptive Token-Wise Pondering with Differentiable Masking
He Li, Feichen Song, Boyi Zeng +4
Test-time scaling has shown that allocating more additional computation at inference can improve generation quality, motivating a natural follow-up question: where should this comp…
Jano: Adaptive Diffusion Generation with Early-stage Convergence Awareness
Yuyang Chen, Linqian Zeng, Yijin ZHou +2
Diffusion models have achieved remarkable success in generative AI, yet their computational efficiency remains a significant challenge, particularly for Diffusion Transformers (DiT…
A Flexible Programmable Pipeline Parallelism Framework for Efficient DNN Training
Lijuan Jiang, Xingjian Qian, Zhenxiang Ma +4
Pipeline parallelism is an essential distributed parallelism method. Increasingly complex and diverse DNN models necessitate meticulously customized pipeline schedules for performa…
TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores
Zimu Liao, Jifeng Ding, Siwei Cui +7
3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where conditional alpha-blending dominates the computational cost in the rendering pipeline. This pa…
LandMarkSystem Technical Report
Zhenxiang Ma, Zhenyu Yang, Miao Tao +5
3D reconstruction is vital for applications in autonomous driving, virtual reality, augmented reality, and the metaverse. Recent advancements such as Neural Radiance Fields(NeRF) a…
GS-Cache: A GS-Cache Inference Framework for Large-scale Gaussian Splatting Models
Miao Tao, Yuanzhen Zhou, Haoran Xu +10
Rendering large-scale 3D Gaussian Splatting (3DGS) model faces significant challenges in achieving real-time, high-fidelity performance on consumer-grade devices. Fully realizing t…
Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models
Weichen Fan, Chenyang Si, Junhao Song +16
We present Vchitect-2.0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation. The overall Vchitect-2.0 system h…
Fisheye-GS: Lightweight and Extensible Gaussian Splatting Module for Fisheye Cameras
Zimu Liao, Siyan Chen, Rong Fu +9
Recently, 3D Gaussian Splatting (3DGS) has garnered attention for its high fidelity and real-time rendering. However, adapting 3DGS to different camera models, particularly fisheye…
FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering
Guofeng Feng, Siyan Chen, Rong Fu +7
This work introduces FlashGS, an open-source CUDA Python library, designed to facilitate the efficient differentiable rasterization of 3D Gaussian Splatting through algorithmic and…
ZeroPP: Unleashing Exceptional Parallelism Efficiency through Tensor-Parallelism-Free Methodology
Ding Tang, Lijuan Jiang, Jiecheng Zhou +5
Large-scale models rely heavily on 3D parallelism for distributed training, which utilizes tensor parallelism (TP) as the intra-operator parallelism to partition model states acros…