To address these limitations we introduce a novel pipeline designed for photorealistic textto4D scene generation discarding the dependency on multiview generative models and instead fully utilizing video generative models trained on diverse realworld datasets Our method begins by generating a reference video using the video generation model
DreamMesh4D Videoto4D Generation with SparseControlled Gaussian
Recent advances in 2D3D generative models enable the generation of dynamic 3D objects from a singleview video Existing approaches utilize score distillation sampling to form the dynamic scene as dynamic NeRF or dense 3D Gaussians However these methods struggle to strike a balance among reference view alignment spatiotemporal consistency and motion fidelity under singleview conditions
DreamScene4D Dynamic MultiObject Scene Generation from Monocular Videos
We attach more examples of videoto4D generation and motion transfer application in the project page than in the main paper Installation it is recommanded to use conda conda create n sc4d python39 conda activate sc4d install dependencies pip install r requirementstxt a modified gaussian splatting
inproceedingsli2024dreammesh4d titleDreamMesh4D Videoto4D Generation with SparseControlled GaussianMesh Hybrid Representation authorZhiqi Li and Yiming Chen and Peidong Liu booktitleAdvances in Neural Information Processing Systems NeurIPS year2024 About NeurIPS 2024 DreamMesh4D Videoto4D Generation with Sparse
This work introduces DreamMesh4D a novel framework combining mesh representation with geometric skinning technique to generate highquality 4D object from a monocular video and binds Gaussian splats to triangle face of mesh for differentiable optimization of both the texture and mesh vertices Recent advancements in 2D3D generative techniques have facilitated the generation of dynamic 3D
SC4D SparseControlled Videoto4D Generation and Motion Transfer
Video To 4d Generation
GitHub WUCVGLDreamMesh4D NeurIPS 2024 DreamMesh4D Videoto4D
PDF DreamMesh4D Videoto4D Generation with SparseControlled
Efficient4D Fast Dynamic 3D Object Generation from a Singleview Video
Inspired by modern graphic pipelines for 3D animation we propose DreamMesh4D which exploits 3D triangular mesh representation and sparsecontrolled geometric skinning methods 47 16 for videoto4D generation To better supervise the generation with 2D signals instead of using classic mesh with UV texture maps we choose a hybrid representation SuGaR 9 which marries 3D Gaussians to
Extensive experiments demonstrate that our method outperforms prior videoto4D generation methods in terms of rendering quality and spatialtemporal consistency Furthermore our meshbased representation is compatible with modern geometric pipelines showcasing its potential in the 3D gaming and film industry
GitHub VITAGroup4DGen 4DGen Grounded 4D Content Generation with
Video To 4d Generation
SC4D SparseControlled Videoto4D Generation and Motion Transfer
As show in figure above we define grounded 4D generation which focuses on videoto4D generation Video is not required to be userspecified but can also be generated by video diffusion With the help of stable video diffusion we implement the function of imagetovideoto4d and texttoimagetovideoto4d
4Real Towards Photorealistic 4D Scene Generation via Video Diffusion
DreamMesh4D Videoto4D Generation with SparseControlled Gaussian
We present DreamScene4D the first method capable of lifting multiobject monocular videos to 4D using dynamic Gaussian Splatting It can handle large and complex motions observed in challenging reallife videos thanks to objectscene decomposition and a motion factorization scheme
To address this limitation we propose an efficient videoto4D object generation framework called Efficient4D It generates highquality spacetimeconsistent images under different camera views and then uses them as labeled data to directly train a novel 4D Gaussian splatting model with explicit point cloud geometry enabling realtime