汇报一下ICCV全部奖项，恭喜朱俊彦团队获最佳论文

Core Insights - The article highlights the significant presence of Chinese authors at ICCV 2025, accounting for 50% of the submissions, showcasing China's growing influence in the field of computer vision [1]. Awards and Recognitions - The Best Paper Award (Marr Prize) was awarded to a study titled "Generating Physically Stable and Buildable Brick Structures from Text," which introduced BRICKGPT, a model that generates stable brick structures based on textual prompts [4][24]. - The Best Student Paper Award went to "FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models," which presents a method for editing images without the need for inversion [6][38]. - Honorary mentions for Best Paper included "Spatially-Varying Autofocus," which innovatively allows cameras to focus on different depths simultaneously [7][42]. - Honorary mentions for Best Student Paper included "RayZer: A Self-supervised Large View Synthesis Model," which autonomously reconstructs camera parameters and generates new perspectives from uncalibrated images [9][47]. Notable Research Contributions - The BRICKGPT model was trained on a dataset of over 47,000 brick structures, demonstrating its ability to generate aesthetically pleasing and stable designs that can be assembled manually or by robotic arms [24][26]. - FlowEdit utilizes a differential equation to map source and target distributions directly, achieving advanced results without the need for model-specific dependencies [39][40]. - The "Fast R-CNN" method, awarded the Helmholtz Prize, significantly improved training and testing speeds while enhancing detection accuracy in object recognition tasks [10][54]. - The research on modified activation functions, which led to a new parameterized ReLU, achieved a top-5 test error of 4.94% on the ImageNet dataset, surpassing human-level performance [58][60]. Awarded Teams and Individuals - The SMPL Body Model Team developed a highly accurate 3D human model based on extensive data from 3D scans, enhancing compatibility with mainstream rendering pipelines [62][66]. - The VQA Team created a dataset for visual question answering, containing approximately 250,000 images and 7.6 million questions, facilitating deeper understanding and reasoning about image content [68][69]. - Distinguished researchers David Forsyth and Michal Irani received the Outstanding Researcher Award for their contributions to computer vision and machine learning [72][75]. - Rama Chellappa was honored with the Azriel Rosenfeld Lifetime Achievement Award for his extensive work in computer vision and pattern recognition [78].