High Dynamic Range Novel View Synthesis (HDR NVS) seeks to learn an HDR 3D model from Low Dynamic Range (LDR) training images captured under conventional imaging conditions. Current methods primarily focus on static scenes, implicitly assuming all scene elements remain stationary and non-living. However, real-world scenarios frequently feature dynamic elements, such as moving objects, varying lighting conditions, and other temporal events, thereby presenting a significantly more challenging scenario. To address this gap, we propose a more realistic problem named HDR Dynamic Novel View Synthesis (HDR DNVS), where the additional dimension ``Dynamic'' emphasizes the necessity of jointly modeling temporal radiance variations alongside sophisticated 3D translation between LDR and HDR. To tackle this complex, intertwined challenge, we introduce HDR-4DGS, a Gaussian Splatting-based architecture featured with an innovative dynamic tone-mapping module that explicitly connects HDR and LDR domains, maintaining temporal radiance coherence by dynamically adapting tone-mapping functions according to the evolving radiance distributions across the temporal dimension. As a result, HDR-4DGS achieves both temporal radiance consistency and spatially accurate color translation, enabling photorealistic HDR renderings from arbitrary viewpoints and time instances. Extensive experiments demonstrate that HDR-4DGS surpasses existing state-of-the-art methods in both quantitative performance and visual fidelity. Source code is available at https://github.com/prinasi/HDR-4DGS.

翻译：高动态范围新视角合成旨在从常规成像条件下捕获的低动态范围训练图像中学习一个高动态范围三维模型。现有方法主要集中于静态场景，隐含假设所有场景元素保持静止且无生命。然而，真实场景常包含动态元素，如运动物体、变化光照条件及其他时序事件，从而构成了显著更具挑战性的场景。为填补这一空白，我们提出了一个更贴近现实的课题——高动态范围动态新视角合成，其中“动态”这一附加维度强调了在低动态范围与高动态范围之间进行复杂三维转换的同时，必须联合建模时序辐射度变化。为应对这一复杂且相互交织的挑战，我们提出了HDR-4DGS——一种基于高斯泼溅的架构，其创新性动态色调映射模块显式连接高动态范围与低动态范围域，通过根据时序维度上演化的辐射度分布动态调整色调映射函数，保持时序辐射度一致性。因此，HDR-4DGS实现了时序辐射度一致性与空间精确的色彩转换，能够从任意视点和时间点生成逼真的高动态范围渲染。大量实验表明，HDR-4DGS在定量性能与视觉保真度上均超越了现有最先进方法。源代码发布于https://github.com/prinasi/HDR-4DGS。