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Text-To-4D Dynamic Scene Generation

Text-To-4D Dynamic Scene Generation

来源:Arxiv_logoArxiv
英文摘要

We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), which is optimized for scene appearance, density, and motion consistency by querying a Text-to-Video (T2V) diffusion-based model. The dynamic video output generated from the provided text can be viewed from any camera location and angle, and can be composited into any 3D environment. MAV3D does not require any 3D or 4D data and the T2V model is trained only on Text-Image pairs and unlabeled videos. We demonstrate the effectiveness of our approach using comprehensive quantitative and qualitative experiments and show an improvement over previously established internal baselines. To the best of our knowledge, our method is the first to generate 3D dynamic scenes given a text description.

Filippos Kokkinos、Adam Polyak、Oron Ashual、Shelly Sheynin、Justin Johnson、Andrea Vedaldi、Uriel Singer、Naman Goyal、Yaniv Taigman、Iurii Makarov、Devi Parikh

计算技术、计算机技术

Filippos Kokkinos,Adam Polyak,Oron Ashual,Shelly Sheynin,Justin Johnson,Andrea Vedaldi,Uriel Singer,Naman Goyal,Yaniv Taigman,Iurii Makarov,Devi Parikh.Text-To-4D Dynamic Scene Generation[EB/OL].(2023-01-26)[2025-05-19].https://arxiv.org/abs/2301.11280.点此复制

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