MoCA: Scalable Compositional 3D Generation
Автор: AI Research Roundup
Загружено: 2025-12-10
Просмотров: 41
In this AI Research Roundup episode, Alex discusses the paper: 'MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation(2512.07628v1)' MoCA is a compositional 3D generative model designed to overcome the quadratic attention bottleneck that arises when modeling many object parts or scene components. It introduces importance-based component routing to select only the most relevant components for global attention, and compresses less important components to preserve context while reducing computation. This enables efficient, fine-grained 3D asset creation with a scalable number of components, supporting up to 32 parts per asset and outperforming prior part-aware 3D methods. The episode breaks down how these architectural choices translate into better compositional object and scene generation. Paper URL: https://arxiv.org/pdf/2512.07628 #AI #MachineLearning #DeepLearning #3DGeneration #DiffusionModels #ComputerGraphics #GenerativeModels
Resources:
GitHub: https://github.com/lizhiqi49/MoCA
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