---
title: STSSNet-AAAI2024
canonical_url: "https://www.modelscope.cn/datasets/ryanhe312/STSSNet-AAAI2024"
md_url: "https://www.modelscope.cn/datasets/ryanhe312/STSSNet-AAAI2024.md"
repository: ryanhe312/STSSNet-AAAI2024
last_updated: 2024-02-01
license: "Apache License 2.0"
storage_size: "1.9 MB"
domain:
  - source_datasets
tasks:
  - original
downloads: 863
stars: 2
---

# STSSNet-AAAI2024

> STSSNet-AAAI2024 - ryanhe312 在 ModelScope 开源的数据集。Description Dataset for "Low-latency Space-time Supersampling for Real-time Rendering"

ryanhe312/STSSNet-AAAI2024 是 ModelScope 魔搭社区上的original数据集，涉及 source_datasets 领域，存储大小 1.9 MB，采用 Apache License 2.0 许可。

- **Repository**: ryanhe312/STSSNet-AAAI2024
- **License**: Apache License 2.0
- **Tasks**: original
- **Domain**: source_datasets
- **Storage size**: 1.9 MB
- **Downloads**: 863
- **Stars**: 2
- **Last updated**: 2024-02-01

Source: https://www.modelscope.cn/datasets/ryanhe312/STSSNet-AAAI2024

---

## Description
Dataset for "Low-latency Space-time Supersampling for Real-time Rendering"

## Abstract
With the rise of real-time rendering and the evolution of display devices, there is a growing demand for post-processing methods that offer high-resolution content in a high frame rate. Existing techniques often suffer from quality and latency issues due to the disjointed treatment of frame supersampling and extrapolation. In this paper, we  recognize the shared context and mechanisms between frame supersampling and extrapolation, and present a novel framework, Space-time Supersampling (STSS). By integrating them into a unified framework, STSS can improve the overall quality with lower latency. To implement an efficient architecture, we treat the aliasing and warping holes unified as reshading regions and put forth two key components to compensate the regions, namely Random Reshading Masking (RRM) and Efficient Reshading Module (ERM). Extensive experiments demonstrate that our approach achieves superior visual fidelity compared to state-of-the-art (SOTA) methods. Notably, the performance is achieved within only 4ms, saving up to 75\% of time against the conventional two-stage pipeline that necessitates 17ms.