3D生成补上物理短板！首个系统性标注物理3D数据集上线，还有一个端到端框架

Core Viewpoint - The article discusses the introduction of PhysXNet, the first systematically annotated physical property 3D dataset, which aims to bridge the gap between virtual 3D generation and physical realism [1][3]. Group 1: Introduction of PhysXNet - PhysXNet contains over 26,000 richly annotated 3D objects, covering five core dimensions: physical scale, materials, affordance, kinematic information, and textual descriptions [3][11]. - An extended version, PhysXNet-XL, includes over 6 million programmatically generated 3D objects with physical annotations [12]. Group 2: Current Research Landscape - Existing 3D generation methods primarily focus on geometric structure and texture, neglecting the modeling based on physical properties [2][8]. - The demand for physical modeling, understanding, and reasoning in 3D space is increasing, necessitating a comprehensive physical-based 3D object modeling system [8][9]. Group 3: Data Annotation Process - The team designed a human-in-the-loop annotation process to efficiently collect and annotate physical information [16][19]. - The annotation framework consists of two main phases: initial data collection and determination of kinematic parameters [19]. Group 4: Generation Methodology - PhysXGen is introduced as a novel framework for generating 3D assets with physical properties, utilizing pre-trained 3D priors to achieve efficient training and good generalization [13][26]. - The method synchronously integrates basic physical properties during the generation process, optimizing structural branches for dual objectives [29][30]. Group 5: Experimental Evaluation - The team conducted qualitative and quantitative evaluations of the model, comparing it against a baseline that uses a separate structure to predict physical properties [33][34]. - PhysXGen demonstrated significant performance improvements in generating physical attributes, achieving relative performance gains of 24%, 64%, 28%, and 72% across various dimensions [38]. Group 6: Future Directions - The article emphasizes the importance of addressing key challenges in physical 3D generation tasks and outlines future research directions [43].