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repliedto Juanxi's post 23 days ago
🚀WorldFoundry | Unified World Model Inference & Evaluation Infrastructure WorldFoundry is an open-source infrastructure that unifies inference and evaluation for generative world models. It supports video generation, interactive worlds, 3D/4D representations, and embodied models through a unified workflow with TUI, CLI, and Studio interfaces. The framework integrates a growing collection of state-of-the-art models and currently includes 58 benchmarks, including VBench, VideoScore, WorldScore, WorldModelBench, Physics-IQ, and T2V-CompBench. We welcome the community to ⭐ star the repository, submit pull requests, open issues, and contribute new models and benchmarks. 🔗 GitHub：https://github.com/OpenEnvision/WorldFoundry 📖 Project & Docs：https://openenvision.github.io/WorldFoundry
reacted to Juanxi's post with 👍 4 months ago
📢 Awesome Multimodal Modeling We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models. 🔹 Taxonomy & Evolution: Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment. Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding. Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms. Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws. 💡 Key Distinction: UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training. 🔗 Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
reacted to Juanxi's post with 😎 4 months ago
📢 Awesome Multimodal Modeling We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models. 🔹 Taxonomy & Evolution: Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment. Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding. Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms. Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws. 💡 Key Distinction: UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training. 🔗 Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
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