We present WildRayZer, a self-supervised framework for novel view synthesis (NVS) in dynamic environments where both the camera and objects move. Dynamic content breaks the multi-view consistency that static NVS models rely on, leading to ghosting, hallucinated geometry, and unstable pose estimation. WildRayZer addresses this by performing an analysis-by-synthesis test: a camera-only static renderer explains rigid structure, and its residuals reveal transient regions. From these residuals, we construct pseudo motion masks, distill a motion estimator, and use it to mask input tokens and gate loss gradients so supervision focuses on cross-view background completion. To enable large-scale training and evaluation, we curate Dynamic RealEstate10K (D-RE10K), a real-world dataset of 15K casually captured dynamic sequences, and D-RE10K-iPhone, a paired transient and clean benchmark for sparse-view transient-aware NVS. Experiments show that WildRayZer consistently outperforms optimization-based and feed-forward baselines in both transient-region removal and full-frame NVS quality with a single feed-forward pass.

翻译：我们提出了WildRayZer，一个用于动态环境中（相机与物体均发生运动）新视角合成的自监督框架。动态内容破坏了静态新视角合成模型所依赖的多视角一致性，导致重影、虚假几何结构及不稳定的姿态估计。WildRayZer通过执行一种分析-合成测试来解决此问题：一个仅考虑相机的静态渲染器解释刚性结构，其残差则揭示瞬态区域。基于这些残差，我们构建伪运动掩码，蒸馏出一个运动估计器，并利用它来掩蔽输入标记并门控损失梯度，从而使监督聚焦于跨视角背景补全。为了支持大规模训练与评估，我们构建了Dynamic RealEstate10K（D-RE10K），一个包含1.5万条随意拍摄的动态序列的真实世界数据集，以及D-RE10K-iPhone，一个为稀疏视角瞬态感知新视角合成提供的成对瞬态与干净基准。实验表明，WildRayZer在单次前向传播中，于瞬态区域去除和全帧新视角合成质量两方面均持续优于基于优化的和前馈基线方法。