  High Dynamic Range (HDR) content (i.e., images and videos) has a broad range
of applications. However, capturing HDR content from real-world scenes is
expensive and time-consuming. Therefore, the challenging task of reconstructing
visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is
gaining attention in the vision research community. A major challenge in this
research problem is the lack of datasets, which capture diverse scene
conditions (e.g., lighting, shadows, weather, locations, landscapes, objects,
humans, buildings) and various image features (e.g., color, contrast,
saturation, hue, luminance, brightness, radiance). To address this gap, in this
paper, we introduce GTA-HDR, a large-scale synthetic dataset of photo-realistic
HDR images sampled from the GTA-V video game. We perform thorough evaluation of
the proposed dataset, which demonstrates significant qualitative and
quantitative improvements of the state-of-the-art HDR image reconstruction
methods. Furthermore, we demonstrate the effectiveness of the proposed dataset
and its impact on additional computer vision tasks including 3D human pose
estimation, human body part segmentation, and holistic scene segmentation. The
dataset, data collection pipeline, and evaluation code are available at:
https://github.com/HrishavBakulBarua/GTA-HDR.
