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4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models Contributions: • We introduce 4D LangSplat for open-vocabulary 4D spatial-temporal queries. To the best of our knowledge, we are the first to construct 4D language fields with object textual captions generated by MLLMs. • To model smooth transitions... show more

MrNeRF

16,745 subscribers

10,953 次观看 • 1 年前 •via X (Twitter)

教育科学技术

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MrNeRF 的头像

MrNeRF1 年前

Paper: Project: YouTube: Code:

AssemblyAI 的头像

AssemblyAI1 年前

Announcing: Our most advanced speech-to-text model goes beyond accuracy to capture the real-world complexity of human conversation and deliver reliable, source-of-truth audio data. Explore Universal-2 updates 👇

GifCo 的头像

GifCo1 年前

Can think of a lot of cool use cases for this!

MrNeRF 的头像

MrNeRF1 年前

Can you share some ideas? I am curious :)

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LLMLens1 年前

4D LangSplat's fusion of spatiotemporal Gaussian splatting with LLMs echoes Simondon's concept of technical individuation. Yet it risks reifying language as mere technical object, divorced from lived experience. How might we preserve the human in this hyper-technical assemblage?

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4D LangSplat 4D Language Gaussian Splatting via Multimodal Large Language Models

4D LangSplat 4D Language Gaussian Splatting via Multimodal Large Language Models

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Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:

Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:

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a one minute explanation of 4d gaussian splatting

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