What do text-to-4D, image-to-4D and video-to-4D generation mean in practice, and for MAV3D, 4D-fy, DreamGaussian4D, Consistent4D, SV4D/SV4D 2.0, L4GM and Diffusion4D, what goes in, what representation comes out, and is it something I can run?
Partially verified: the curator re-read none of the cited sources; S1, S3, S11, S13, S15, S5, S8, S4, S6, S12, S17, S9 rest on the producer's pinned reading. "4D" here means a 3D object or scene that changes over time and can be rendered from new viewpoints; every system read produces a generated asset, not a captured one. Three routes appear in the primary sources: (a) per-asset optimisation against diffusion priors, by score distillation (MAV3D, 4D-fy, Consistent4D) or, for DreamGaussian4D, by fitting Gaussian deformation to a generated driving video with image-diffusion guidance, (b) a diffusion model first generates a grid of views by frames, then a 4D representation is fitted to it (SV4D, SV4D 2.0, Diffusion4D), and (c) a feed-forward network that reconstructs per-frame 3D Gaussians from a video (L4GM). Output representations are dynamic NeRFs or Gaussians; only the DreamGaussian4D abstract mentions animated meshes (per-frame, candidate 02) and no abstract describes a rigged as...
Structured fields
{
"citations": [
{
"url": "https://arxiv.org/html/2301.11280v1",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2311.17984v2",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2312.17142v3",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2311.02848v1",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2407.17470v2",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2406.10324v1",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2405.16645v1",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/sherwinbahmani/4dfy",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/nv-tlabs/L4GM-official",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/jiawei-ren/dreamgaussian4d",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/stability-ai/generative-models",
"note": "cited at https://github.com/Stability-AI/generative-models; candidate evidence excerpts"
},
{
"url": "https://github.com/VITA-Group/Diffusion4D",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/Stability-AI/generative-models",
"note": "observed URL (dated/alias form of the cited catalogue source; canonical identity retained)"
}
]
}Evidence claims
- [sourced_fact] "4D" here means a 3D object or scene that changes over time and can be rendered from new viewpoints; every system read produces a generated asset, not a captured one. Three routes appear in the primary sources: (a) per-asset optimisation against diffusion priors, by score distillation (MAV3D, 4D-fy, Consistent4D) or, for DreamGaussian4D, by fitting Gaussian deformation to a generated driving video with image-diffusion guidance, (b) a diffusion model first generates a grid of views by frames, then a 4D representation is fitted to it (SV4D, SV4D 2.0, Diffusion4D), and (c) a feed-forward network that reconstructs per-frame 3D Gaussians from a video (L4GM). Output representations are dynamic NeRFs or Gaussians; only the DreamGaussian4D abstract mentions animated meshes (per-frame, candidate 02) and no abstract describes a rigged asset. Whether you can run a system depends on its release, which differs sharply (see candidates 02 and 07). · locator: candidate evidence excerpts source
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |
Linked records
- cites → https://arxiv.org/html/2301.11280v1
- cites → https://arxiv.org/html/2311.02848v1
- cites → https://arxiv.org/html/2311.17984v2
- cites → https://arxiv.org/html/2312.17142v3
- cites → https://arxiv.org/html/2405.16645v1
- cites → https://arxiv.org/html/2406.10324v1
- cites → https://arxiv.org/html/2407.17470v2
- cites → https://github.com/jiawei-ren/dreamgaussian4d
- cites → https://github.com/nv-tlabs/l4gm-official
- cites → https://github.com/sherwinbahmani/4dfy
- cites → https://github.com/stability-ai/generative-models
- cites → https://github.com/vita-group/diffusion4d