If I only have a text prompt, what do MAV3D and 4D-fy actually show, how were they evaluated, which one can I run, and how good is the motion?
Corrected 2026-10-23 - see C3-B588-07; the original statements below are kept for history and are superseded where the correction says so. Partially verified: the curator re-read none of the cited sources; S1, S3, S4, S19, S8, S16, S9, S5 rest on the producer's pinned reading. Two papers cover text-to-4D with score distillation. MAV3D (Meta, January 2023) optimises a six-plane dynamic NeRF against a text-to-video model in three stages (about 6.5 h on 8 A100) and shows an R-Precision of 83.7 against adapted internal baselines of 6.2-10.6; no code is available. 4D-fy (arXiv v2, May 2024) blends 3D-aware, image and video diffusion priors, takes about 23 h on one 80 GB A100, and ships a repository with run instructions. In 4D-fy's own user study (26 evaluators, 28 prompts) users prefer 4D-fy for appearance (92%) and structure (89%) but only 41% prefer it for motion, and the motion difference is the one comparison that is not statistically significant. 4D-fy says motion is limited to sim...
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://github.com/sherwinbahmani/4dfy",
"note": "candidate evidence excerpts"
},
{
"url": "https://huggingface.co/cerspense/zeroscope_v2_576w",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2405.16645v1",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2503.16396v3",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/VITA-Group/Diffusion4D",
"note": "candidate evidence excerpts"
},
{
"url": "https://arxiv.org/html/2406.10324v1",
"note": "candidate evidence excerpts"
}
]
}Evidence claims
- [sourced_fact] Two papers cover text-to-4D with score distillation. MAV3D (Meta, January 2023) optimises a six-plane dynamic NeRF against a text-to-video model in three stages (about 6.5 h on 8 A100) and shows an R-Precision of 83.7 against adapted internal baselines of 6.2-10.6; no code is available. 4D-fy (arXiv v2, May 2024) blends 3D-aware, image and video diffusion priors, takes about 23 h on one 80 GB A100, and ships a repository with run instructions. In 4D-fy's own user study (26 evaluators, 28 prompts) users prefer 4D-fy for appearance (92%) and structure (89%) but only 41% prefer it for motion, and the motion difference is the one comparison that is not statistically significant. 4D-fy says motion is limited to simple movements and that current metrics are not sufficient; MAV3D says quality depends on the text-to-video model across views. Diffusion4D reports an 8-minute text-to-4D result but its generator is not released. · 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/2311.17984v2
- cites → https://arxiv.org/html/2405.16645v1
- cites → https://arxiv.org/html/2406.10324v1
- cites → https://arxiv.org/html/2503.16396v3
- cites → https://github.com/sherwinbahmani/4dfy
- cites → https://github.com/vita-group/diffusion4d
- cites → https://huggingface.co/cerspense/zeroscope_v2_576w