3DDFA
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Chinese Academy of Sciences,Michigan State University
- Organisation type
- Academia,Academia
- Country
- China, United States of America
- Published
- 23 November 2015
- Authors
- Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, S. Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face detection, 3D reconstruction
- Approach
- Supervised
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 5.4M
- Training data
- 293,901 tokens
Parameters based on Figure 2: CNN layers: 4*4*16*6+4*4*32*16=9728 Local CNN: 3*3*48*32*11*11+3*3*64*49*9*9=3958848 FC: 9*9*64*256+234*256=1387008 Total: 9728+3958848+1387008=5355584
"We divide 300W-LP into 97,967 samples for training and 24,483 samples for testing, without identity overlapping. "
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
MIT license: https://github.com/cleardusk/3DDFA (refers to the improved later version of the paper)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
https://paperswithcode.com/sota/3d-face-reconstruction-on-florence "Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods."
Sources
Where this record came from and when it was last checked.
- Reference
- Face Alignment in Full Pose Range: A 3D Total Solution
- Last updated
- 28 November 2025
What the numbers mean
What this model is
3DDFA was published by Chinese Academy of Sciences,Michigan State University, in China, in November 2015. It comes out of academia,Academia.
It works in Vision, and is recorded as doing face detection, 3D reconstruction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Around 293,901 tokens went into training it.
Its inclusion criterion is sOTA improvement.
Answers
3DDFA — common questions
When was 3DDFA released?
3DDFA was published in November 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is 3DDFA used for?
3DDFA works in Vision, and is recorded as handling face detection, 3D reconstruction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run 3DDFA?
None. 3DDFA is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is 3DDFA open source?
No. 3DDFA has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does 3DDFA have?
3DDFA has 5.4M parameters. Parameters based on Figure 2: CNN layers: 4*4*16*6+4*4*32*16=9728 Local CNN: 3*3*48*32*11*11+3*3*64*49*9*9=3958848 FC: 9*9*64*256+234*256=1387008 Total: 9728+3958848+1387008=5355584. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created 3DDFA?
3DDFA was published by Chinese Academy of Sciences,Michigan State University, based in China, categorised as academia,Academia.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.