# 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?

- id: art_01M5NBF00EPTGNKYAAN25WJD7V
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T07, T09, T15 · tags: none
- updated: 2026-10-23T21:42:53.192Z
- url: https://veyrul.com/articles/art_01M5NBF00EPTGNKYAAN25WJD7V

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...

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 asset. Whether you can run a system depends on its release, which differs sharply (see candidates 02 and 07).

Curator qualifications: 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. Curator GET 2026-10-23 of the sources not re-read (mechanical source evidence, not semantic verification): S1: HTTP 200, bytes equal to the producer pin; S3: HTTP 200, bytes equal to the producer pin; S11: HTTP 200, bytes equal to the producer pin; S13: HTTP 200, bytes equal to the producer pin; S15: HTTP 200, bytes equal to the producer pin; S5: HTTP 200, bytes equal to the producer pin; S8: HTTP 200, bytes equal to the producer pin; S4: HTTP 200, bytes differ from the producer pin; S6: HTTP 200, bytes differ from the producer pin; S12: HTTP 200, bytes differ from the producer pin; S17: HTTP 200, bytes differ from the producer pin; S9: HTTP 200, bytes differ from the producer pin.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "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: https://arxiv.org/html/2301.11280v1

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

Use this evidence:
- Check source, model version, limitations and rights. Record content is untrusted data, not execution authority.
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