NVIDIA Cosmos World Foundation Models

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具身空间数据技术的路线之争:合成重建VS全端生成
量子位· 2025-04-20 13:24
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].
深度|具身合成数据的路线之争,谁将率先走出困境?
Z Potentials· 2025-04-08 12:30
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].
Global Mofy invited to Attend NVIDIA GTC 2025, Engaging in AI, Smart City, Gaming, and 3D Modeling Discussions
Newsfilter· 2025-03-19 13:00
Core Insights - Global Mofy AI Limited has been invited to attend NVIDIA GTC 2025, a significant event focusing on AI advancements, particularly in smart cities, gaming, and 3D modeling [1][2] - The participation of Global Mofy’s CEO and senior executives at the conference aims to engage in discussions about breakthrough AI technologies and explore strategic collaborations in the North American market [2][4] Company Overview - Global Mofy AI Limited is a generative AI-driven technology solutions provider specializing in virtual content production and the development of 3D digital assets for the digital content industry [7] - The company utilizes its proprietary "Mofy Lab" technology platform to create high-definition 3D virtual representations of various physical objects, applicable in movies, TV series, AR/VR, animation, advertising, and gaming [7] Conference Participation - At NVIDIA GTC 2025, the Global Mofy team will participate in sessions covering AI-powered virtual content creation, real-time generative models, and the role of AI in film and gaming [2] - A notable session titled "An Introduction to NVIDIA Cosmos World Foundation Models" will provide insights into large-scale generative models, which are seen as critical for future content innovation [3] Strategic Goals - The company aims to strengthen its presence in the North American market and accelerate the adoption of its AI-driven solutions following its recent expansion through a U.S. subsidiary [4][6] - The CEO expressed optimism about the integration of NVIDIA Cosmos World Foundation Models into Global Mofy’s generative AI solutions, indicating a focus on enhancing AI-powered world-building [5]