GitHub VITAGroup4DGen 4DGen Grounded 4D Content Generation with
231217225 4DGen Grounded 4D Content Generation with Spatial
We release our collected data in Google DriveSome of these data are userspecified while others are generated Each test case contains two folders namepose0 and namesyncpose0 refers to the monocular video sequencesync refers to the pseudo labels generated by SyncDreamer We recommend using PracticalRIFE if you need to introduce more frames in your video sequence
GenXD
Recent developments in 2D visual generation have been remarkably successful However 3D and 4D generation remain challenging in realworld applications due to the lack of largescale 4D data and effective model design In this paper we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life Due to the lack of real
开源4D生成框架4DGen 基于动态 3D 高斯的可控 4D 生成 知乎
We introduce grounded 4D content generation We identify monocular video sequences as a key component in constructing the 4D content Our pipeline facilitates conditional 4D generation enabling users to specify the motion monocular videos thus offering superior control over content creation
总结 4DGen定义了 Grounded 4D Generation的任务形式通过视频序列和可选3D模型的引入提升了4D生成的可控性通过高效的4D Gaussian Splatting的表达2D和3D伪标签的监督和时空的连续性约束使得4DGen可以实现高分辨率长时序的高质量的4D内容生成
Gen4d
4DGen Grounded 4D Content Generation with Spatialtemporal Consistency
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Gen4d
241102319 GenXD Generating Any 3D and 4D Scenes arXivorg
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Recent developments in 2D visual generation have been remarkably successful However 3D and 4D generation remain challenging in realworld applications due to the lack of largescale 4D data and effective model design In this paper we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life Due to the lack of real
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Aided by texttoimage and texttovideo diffusion models existing 4D content creation pipelines utilize score distillation sampling to optimize the entire dynamic 3D scene However as these pipelines generate 4D content from text or image inputs directly they are constrained by limited motion capabilities and depend on unreliable prompt engineering for desired results To address these