VDPP: Video Depth Post-Processing for Speed and Scalability

Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-to-end video depth models have established state-of-the-art performance. Although current end-to-end (E2E) models have achieved state-of-the-art performance, they function as tightly coupled systems that suffer from a significant adaptation lag whenever superior single-image depth estimators are released. To mitigate this issue, post-processing methods such as NVDS offer a modular plug-and-play alternative to incorporate any evolving image depth model without retraining. However, existing post-processing methods still struggle to match the efficiency and practicality of E2E systems due to limited speed, accuracy, and RGB reliance. In this work, we revitalize the role of post-processing by proposing VDPP (Video Depth Post-Processing), a framework that improves the speed and accuracy of post-processing methods for video depth estimation. By shifting the paradigm from computationally expensive scene reconstruction to targeted geometric refinement, VDPP operates purely on geometric refinements in low-resolution space. This design achieves exceptional speed (>43.5 FPS on NVIDIA Jetson Orin Nano) while matching the temporal coherence of E2E systems, with dense residual learning driving geometric representations rather than full reconstructions. Furthermore, our VDPP's RGB-free architecture ensures true scalability, enabling immediate integration with any evolving image depth model. Our results demonstrate that VDPP provides a superior balance of speed, accuracy, and memory efficiency, making it the most practical solution for real-time edge deployment. Our project page is at https://github.com/injun-baek/VDPP

MSG score: A Comprehensive Evaluation for Multi-Scene Video Generation

This paper addresses the metrics required for generating multi-scene videos based on a continuous scenario, as opposed to traditional short video generation. Scenario-based videos require a comprehensive evaluation that considers multiple factors such as character consistency, artistic coherence, aesthetic quality, and the alignment of the generated content with the intended prompt. Additionally, in video generation, unlike single images, the movement of characters across frames introduces potential issues like distortion or unintended changes, which must be effectively evaluated and corrected. In the context of probabilistic models like diffusion, generating the desired scene requires repeated sampling and manual selection, akin to how a film director chooses the best shots from numerous takes. We propose a score-based evaluation benchmark that automates this process, enabling a more objective and efficient assessment of these complexities. This approach allows for the generation of high-quality multi-scene videos by selecting the best outcomes based on automated scoring rather than manual inspection.