编辑丨 量子位 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 刚刚，备受瞩目的ICCV 2025，在美国夏威夷正式"开奖"！ 好家伙，在提交论文的作者里，中国直接占了半壁江山，不多不少占比50%。 各个奖项也是重中之重捷报频传，现场更是人山人海……好在前方参会的"詹姆斯邦迪" ( 小红书博主，欢迎大家去 follow) ，第一时间分享了最 新进展。 让我们一起膜拜看看顶会荣耀，今年花落谁家？ 最佳论文奖（马尔奖） ： Generating Physically Stable and Buildable Brick Structures from Text（从文本生成物理稳定且可搭建的积木结构）。 △ 图源小红书博主：@詹姆斯邦迪 最佳学生论文奖 ： FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models（基于预训练流模型的无反演文本编辑方法） 。 △ 图源小红 ...

Core Insights - The article discusses the recent International Conference on Computer Vision (ICCV) held in Hawaii, highlighting the award-winning research papers and their contributions to the field of computer vision [2][5][24]. Group 1: Award Winners - The Best Paper Award was given to a research team from Carnegie Mellon University (CMU) for their paper titled "Generating Physically Stable and Buildable Brick Structures from Text," led by notable AI scholar Zhu Junyan [3][7][11]. - The Best Student Paper Award was awarded to a paper from the Technion, titled "FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models," which introduces a novel image editing method [28][30]. Group 2: Conference Statistics - ICCV is one of the top three conferences in computer vision, held biennially, with this year's conference receiving 11,239 valid submissions and accepting 2,699 papers, resulting in a 24% acceptance rate, a significant increase from the previous conference [5]. Group 3: Research Contributions - The paper by CMU presents Brick GPT, the first method capable of generating physically stable and interconnected brick assembly models based on text prompts. The research includes a large dataset of over 47,000 brick structures and 28,000 unique 3D objects with detailed descriptions [11][13]. - The FlowEdit paper from Technion proposes a new image editing approach that bypasses the traditional image-to-noise inversion process, achieving higher fidelity edits by establishing a direct mapping path between source and target image distributions [32][34]. Group 4: Methodology and Results - The Brick GPT method utilizes a self-regressive large language model trained on a dataset of brick structures, incorporating validity checks and a physics-aware rollback mechanism to ensure stability in generated designs [13][19]. - Experimental results show that Brick GPT outperforms baseline models in terms of validity and stability, achieving a 100% validity rate and 98.8% stability in generated structures [20][22].

Core Points - The ICCV 2025 conference in Hawaii highlighted significant contributions from Chinese researchers, who accounted for 50% of the paper submissions [1] - Various prestigious awards were announced, showcasing advancements in computer vision research [3] Award Highlights - Best Paper Award (Marr Prize): "Generating Physically Stable and Buildable Brick Structures from Text" introduced BRICKGPT, a model that generates stable brick structures based on text prompts, utilizing a dataset of over 47,000 structures [4][24][26] - Best Student Paper Award: "FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models" proposed a method for image editing without inversion, achieving state-of-the-art results [6][39][40] - Best Paper Honorary Mention: "Spatially-Varying Autofocus" developed a technique for dynamic depth adjustment in imaging, enhancing focus clarity across scenes [7][42][44] - Best Student Paper Honorary Mention: "RayZer: A Self-supervised Large View Synthesis Model" demonstrated 3D perception capabilities using uncalibrated images [9][47][49] Special Awards - Helmholtz Prize: Awarded to "Fast R-CNN" for its efficient object detection capabilities, significantly improving training and testing speeds [10][52][54] - Another Helmholtz Prize was given for research on rectified activation functions, achieving performance surpassing human-level accuracy on ImageNet [10][59][60] - Evelyn Erham Award: Recognized teams for their contributions to 3D modeling and visual question answering [12][63][68] - Distinguished Researcher Award: David Forsyth and Michal Irani were honored for their impactful work in computer vision [14][73][76] - Azriel Rosenfeld Lifetime Achievement Award: Rama Chellappa was recognized for his extensive contributions to the field [16][79] Research Contributions - The BRICKGPT model was developed to generate physically stable structures, utilizing a large dataset and innovative mechanisms for stability [24][26] - FlowEdit's approach allows for seamless image editing across different model architectures, enhancing flexibility in applications [39][40] - The spatially-varying autofocus technique improves image clarity by dynamically adjusting focus based on scene depth [42][44] - RayZer's self-supervised learning approach enables 3D scene reconstruction without the need for calibrated camera data [47][49] Conclusion - The ICCV 2025 conference showcased groundbreaking research and innovations in computer vision, with significant contributions from various teams and individuals, particularly highlighting the achievements of Chinese researchers [1][3]

Core Insights - The ICCV (International Conference on Computer Vision) awarded the best paper and best student paper on October 22, 2023, highlighting significant advancements in computer vision research [1][2][4]. Group 1: Best Paper - The best paper award was given to a research team from Carnegie Mellon University (CMU) for their paper titled "Generating Physically Stable and Buildable Brick Structures from Text" led by notable AI scholar Junyan Zhu [6][9]. - The paper introduces BrickGPT, a novel method that generates physically stable and interconnected brick assembly models based on text prompts, marking a significant advancement in the field [9][11]. - The research team created a large-scale dataset of stable brick structures, comprising over 47,000 models and 28,000 unique 3D objects with detailed text descriptions, to train their model [11][10]. Group 2: Methodology and Results - The methodology involves discretizing a brick structure into a sequence of text tokens and training a large language model to predict the next brick to add, ensuring physical stability through validity checks and a rollback mechanism [10][17]. - Experimental results indicate that BrickGPT achieved a 100% validity rate and 98.8% stability rate, outperforming various baseline models in both effectiveness and stability [20][18]. - The paper's approach allows for the generation of diverse and aesthetically pleasing brick structures that align closely with the input text prompts, demonstrating high fidelity in design [11][20]. Group 3: Best Student Paper - The best student paper award went to a research from the Technion titled "FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models," which innovatively bypasses traditional image editing paths to enhance image fidelity [25][28]. - FlowEdit establishes a direct mapping path between source and target image distributions, resulting in lower transfer costs and better preservation of original image structure during editing [31][27]. - The method was validated on advanced T2I flow models, achieving state-of-the-art results across various complex editing tasks, showcasing its efficiency and superiority [31][31]. Group 4: Other Awards and Recognitions - The Helmholtz Prize was awarded for contributions to computer vision benchmarks, recognizing two significant papers, including "Fast R-CNN" by Ross Girshick, which improved detection speed and accuracy [36][38]. - The Everingham Prize recognized teams for their contributions to 3D modeling and multimodal AI, including the development of the SMPL model and the VQA dataset [41][43]. - Significant Researcher Awards were given to David Forsyth and Michal Irani for their impactful contributions to the field of computer vision [50][52].