# If I have one video (or one image) of an object and no rig, which video-to-4D generation systems can produce an animated 3D object, what do they output, and what do they cost to run?

- id: art_01M5MBXQZT6H575YQ38SW7H5C9
- type: article · status: active · revision: 2
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T07, T11, T15, T19 · tags: none
- updated: 2026-10-23T23:34:39.861Z
- url: https://veyrul.com/articles/art_01M5MBXQZT6H575YQ38SW7H5C9

Corrected 2026-10-23 - see C3-B588-09; the original statements below are kept for history and are superseded where the correction says so. Four released video-to-4D lines were read at pinned versions. Consistent4D (monocular stationary-camera video, SDS from Zero123-XL into a cascade dynamic NeRF; at least 24 GB VRAM, hours on a V100) and DreamGaussian4D (image or video to 4D Gaussians in minutes on an 80 GB A100, with an obj mesh-sequence export) are optimisation-based. SV4D and SV4D 2.0 instead generate a multi-view video image matrix with a video diffusion model and then fit a dynamic NeRF to it (15-25 minutes; up to 30 GB on an H100 for SV4D 2.0). All are object-centric; SV4D 2.0 states it is limited to object-centric videos and cannot handle strong perspective distortion. Outputs are generated, not captured: the authors' own quality statements are 'not as high as photo-realistic 4D methods' (DG4D) and failures on abrupt motion (Consistent4D). Weights and rights are in candidate...

Four released video-to-4D lines were read at pinned versions. Consistent4D (monocular stationary-camera video, SDS from Zero123-XL into a cascade dynamic NeRF; at least 24 GB VRAM, hours on a V100) and DreamGaussian4D (image or video to 4D Gaussians in minutes on an 80 GB A100, with an obj mesh-sequence export) are optimisation-based. SV4D and SV4D 2.0 instead generate a multi-view video image matrix with a video diffusion model and then fit a dynamic NeRF to it (15-25 minutes; up to 30 GB on an H100 for SV4D 2.0). All are object-centric; SV4D 2.0 states it is limited to object-centric videos and cannot handle strong perspective distortion. Outputs are generated, not captured: the authors' own quality statements are 'not as high as photo-realistic 4D methods' (DG4D) and failures on abrupt motion (Consistent4D). Weights and rights are in candidate 06.

Curator correction 2026-10-23 (discovery audit record C3-B588-09; sources re-fetched 2026-10-23, excerpts re-located by curator GET): Correction (overstated quote). C3-B103-W21-04 quotes the authors as saying quality is 'not as high as photo-realistic 4D methods'. The cited paper's limitation sentence is narrower: quality is not as high as many photo-realistic 4D generation methods. The word 'many' matters because it does not claim inferiority to every such method. Version/date. arXiv 2312.17142v3 fetched 2026-10-23.

Curator qualifications: All excerpts located; speed claims not comparable across hardware/outputs, as stated.

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/2311.02848v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/yanqinJiang/Consistent4D",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2312.17142v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/jiawei-ren/dreamgaussian4d",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2407.17470v2",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2503.16396v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/Stability-AI/generative-models",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Four released video-to-4D lines were read at pinned versions. Consistent4D (monocular stationary-camera video, SDS from Zero123-XL into a cascade dynamic NeRF; at least 24 GB VRAM, hours on a V100) and DreamGaussian4D (image or video to 4D Gaussians in minutes on an 80 GB A100, with an obj mesh-sequence export) are optimisation-based. SV4D and SV4D 2.0 instead generate a multi-view video image matrix with a video diffusion model and then fit a dynamic NeRF to it (15-25 minutes; up to 30 GB on an H100 for SV4D 2.0). All are object-centric; SV4D 2.0 states it is limited to object-centric videos and cannot handle strong perspective distortion. Outputs are generated, not captured: the authors' own quality statements are 'not as high as photo-realistic 4D methods' (DG4D) and failures on abrupt motion (Consistent4D). Weights and rights are in candidate 06. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2311.02848v1

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

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