2025/10/07 by Danush Kumar Venkatesh, Adam Schmidt, Venkatesh, Danush Kumar +5
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #Multimodal Machine Learning Applications #Quantitative Methods (q-bio.QM) #Surgical Simulation and Training #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2510.07345
openalex publication_date 2025/10/07 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
Surgical video datasets are essential for scene understanding, enabling procedural modeling and intra-operative support. However, these datasets are often heavily imbalanced, with rare actions and tools under-represented, which limits the robustness of downstream models. We address this challenge with SurgiFlowVid, a sparse and controllable video diffusion framework for generating surgical videos of under-represented classes. Our approach introduces a dual-prediction diffusion module that jointly denoises RGB frames and optical flow, providing temporal inductive biases to improve motion modeling from limited samples. In addition, a sparse visual encoder conditions the generation process on lightweight signals (e.g., sparse segmentation masks or RGB frames), enabling controllability without dense annotations. We validate our approach on three surgical datasets across tasks including action recognition, tool presence detection, and laparoscope motion prediction. Synthetic data generated by our method yields consistent gains of 10-20% over competitive baselines, establishing SurgiFlowVid as a promising strategy to mitigate data imbalance and advance surgical video understanding methods.