Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting TL;DR: How to freeze a dynamic molecular scene when people inevitably move. Contributions: • A Novel Problem Formulation and Benchmark: We are the first to formally address synthesizing high-fidelity, freeze-time videos from monocular MC footage, providing a new benchmark and evaluation protocol. • A Targeted Regularization Framework: We propose a novel method to identify and regularize hidden and defective Gaussians, the primary sources of temporal artifacts, anchoring them to reliable past or future states. • State-of-the-art Performance with Zero Inference Overhead: We improve visual quality and stability in existing methods without architectural changes. As the deformation runs only once for a target instant, we achieve inference speeds exceeding 280 FPS on an RTX 4090.

EnvGS: Modeling View-Dependent Appearance with Environment Gaussian Contributions: • We propose a novel scene representation for accurately modeling complex near-field and high-frequency reflections in real-world environments. • We developed a real-time ray-tracing renderer for 2DGS, enabling joint optimization of our representation for accurate scene reconstruction while achieving real-time rendering speeds. • Extensive experiments show that EnvGS significantly outperforms previous methods. To the best of our knowledge, EnvGS is the first method to achieve real-time photorealistic specular reflections synthesis in real-world scenes.