Core Viewpoint - The breakthrough in embodied intelligence relies heavily on high-quality data, with a significant focus on synthetic data generation due to the high costs of real data collection [1][2]. Group 1: Data Challenges - The current state of embodied intelligence data is characterized by scarcity and inadequacy, with existing sources being limited and not sufficiently diverse [16][18]. - Three main categories of existing data sources are identified: real scan data, game engine environments, and open-source synthetic datasets, each with its limitations [17]. - The indoor embodied intelligence scenarios require structured, semantic, and interactive 3D scene data, which is challenging to collect due to the unique layouts and usage patterns of individual households [18][19]. Group 2: Technical Approaches - There are two primary technical routes for synthetic data generation: "video synthesis + 3D reconstruction" and "end-to-end 3D generation" [3][24]. - The "video synthesis + 3D reconstruction" approach involves generating video or images first, which can lead to cumulative errors and limited structural accuracy [24][39]. - The "end-to-end 3D generation" method aims for direct synthesis of structured spatial data but faces challenges such as low generation quality and lack of common sense [67][68]. Group 3: Innovations in Data Generation - A new technical solution called "modal encoding" is proposed to address the common sense gap in end-to-end 3D generation, allowing for the digital encoding and implicit learning of spatial solutions [5][91]. - The Sengine SimHub is introduced as a system that integrates design knowledge into the generation process, enhancing the stability and adaptability of the generated data [75][78]. - The focus is on creating a data generation system that not only produces space but also generates "understandable and usable" environments, incorporating design logic and user preferences [91][96]. Group 4: Future Directions - The industry is at a critical juncture where the need for a new approach to data generation is evident, moving beyond mere data accumulation to creating "useful data" [95][96]. - The future of embodied intelligence may hinge on how space is defined and understood, emphasizing the importance of integrating rules and preferences into spatial data generation [96][100].

Core Viewpoint - The article discusses the competition between two main technical routes for embodied synthetic data: "Video Synthesis + 3D Reconstruction" and "End-to-End 3D Generation" [1][49]. Group 1: Challenges in Embodied Intelligence - The development of robots has seen faster advancements in physical capabilities compared to cognitive abilities, leading to difficulties in unfamiliar environments [3]. - Embodied intelligence requires an integrated ability of perception, reasoning, and decision-making, which is contingent on a clear understanding of spatial structures [4]. - Current AI advancements are hindered by a lack of high-quality spatial data, which is essential for effective cognitive functioning [5]. Group 2: Data Dilemma - The existing data for embodied intelligence is limited and insufficient, categorized into three types: real scanned data, game engine environments, and open-source synthetic datasets, all of which have significant limitations [6]. - The unique layout and usage patterns of homes create challenges in collecting comprehensive training data, making traditional data collection methods impractical [8]. Group 3: Technical Routes - The two main technical paths for synthetic data generation are: 1. Video Synthesis + 3D Reconstruction: This method generates video or images first, then reconstructs them into 3D data, facing issues with accuracy and physical consistency [11][13]. 2. End-to-End 3D Generation: This approach directly synthesizes structured spatial data using advanced techniques like Graph Neural Networks (GNNs) and diffusion models, but struggles with generating high-quality outputs [22][39]. Group 4: Innovations in 3D Generation - New methods such as "modal encoding" aim to integrate design knowledge into the generation process, enhancing the model's ability to create reasonable spatial structures [2][44]. - The Sengine SimHub framework incorporates training processes that improve the stability and adaptability of the generated data, aligning it more closely with real-world logic and semantics [45][48]. Group 5: Future Directions - The industry faces a "data drought" compared to the more established data loops in autonomous driving, necessitating innovative approaches to spatial understanding and generation [49]. - The future of embodied intelligence may hinge on how spatial concepts are defined and understood, emphasizing the need for a system that embeds rules and preferences into spatial data generation [50].

Key Points - The article emphasizes China's commitment to increasing foreign investment and maintaining a favorable investment environment for foreign businesses, highlighting that China will continue to be an ideal destination for foreign investment [3] - The China Securities Regulatory Commission (CSRC) has amended the regulations regarding IPOs, allowing bank wealth management products and insurance asset management products to be prioritized for allocation, and has set rules to prevent strategic investors from lending shares during lock-up periods [3] - The U.S. core PCE price index has shown a year-on-year increase of 2.8%, raising concerns about persistent inflation and potential stagflation, leading traders to bet on a possible interest rate cut by the Federal Reserve in July [3] - The market regulatory authority in China is reviewing the proposed sale of Panama port by CK Hutchison to BlackRock to ensure fair competition and protect public interest [3] Domestic Stock Market - The CSRC is planning to impose administrative penalties on Dongxu Group and Dongxu Optoelectronics for fraudulent issuance and inflated revenue and profits, with fines totaling 1.7 billion yuan and potential lifetime bans for key individuals [15] - The A-share market saw a decline across major indices, with the Shanghai Composite Index falling by 0.67% to 3351.31 points, and over 4300 stocks declining [15] - The Hong Kong Hang Seng Index closed down 0.65%, with technology and energy stocks underperforming while pharmaceutical stocks gained [15] Financial Sector - The six major state-owned banks in China reported a total net profit of approximately 1.4 trillion yuan for 2024, with an average daily profit of about 38 billion yuan, while their net interest margins faced pressure [16] - The CSRC has revised the rules for information disclosure by listed companies, enhancing risk disclosure requirements and establishing a system for deferring and exempting disclosures [16] International Market - The Nasdaq Golden Dragon China Index fell by 3.11%, with significant declines in major Chinese stocks listed in the U.S. [37] - The EU is expected to impose minimal fines on Apple and Meta to avoid escalating tensions with the U.S. [38] Commodity Market - Domestic commodity futures closed mostly lower, with energy and chemical products showing weakness, while basic metals had mixed performance [40] - The Ministry of Industry and Information Technology is promoting high-quality development in the aluminum industry, aiming for a 3%-5% increase in domestic bauxite resources by 2027 [40]