ICface: Interpretable and Controllable Face Reenactment Using GANs
Open accessAbstract
This paper presents a generic face animator that is able to control the pose and expressions of a given face image. The animation is driven by human interpretable control signals consisting of head pose angles and the Action Unit (AU) values. The control information can be obtained from multiple sources including external driving videos and manual controls. Due to the interpretable nature of the driving signal, one can easily mix the information between multiple sources (e.g. pose from one image and expression from another) and apply selective postproduction editing. The proposed face animator is implemented as a two stage neural network model that is learned in self-supervised manner using a large video collection. The proposed Interpretable and Controllable face reenactment network (ICface) is compared to the state-of-the-art neural network based face animation techniques in multiple tasks. The results indicate that ICface produces better visual quality, while being more versatile than most of the comparison methods. The introduced model could provide a lightweight and easy to use tool for multitude of advanced image and video editing tasks. The program code will be publicly available upon the acceptance of the paper.
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ICface uses a conditional GAN to take neutral image and previous facial attribute vector (AUs) as input to generate expression.
“Then, it uses a conditional GAN to take the neutral image and the previous facial attribute vector (i.e., AUs) as input to generate expression, which solves the second problem.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tripathy et al. 2020
Section 3 (Method; page unavailable) says ICface combines a neutralized source image with a driving attribute vector and passes the result to a generator to produce the animated face. But the vector contains both head-pose parameters and AU activations, not AUs alone; the paper calls it a driving vector, not a previous one. (LLM, conf=0.84)
Experimental — accuracy not yet measuredICface proposes a two-stage neural network face animator that can control pose and expressions of a given face image.
“Interpretable and controllable face reenactment network (ICface) (Tripathy et al. 2020) has proposed a two-stage neural network face animator which can control the pose and expressions of a given face image.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tripathy et al. 2020
ICface face animator is data-driven and GAN-based and suitable for a large number of identities.
“The face animator is a data-driven and GAN-based system that is suitable for a large number of identities.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tripathy et al. 2020
ICFA proposes to use action units (AUs) to represent emotions.
“To solve these problems, ICFA (Tripathy et al. 2020) proposes to use action units (AUs) (Friesen and Ekman 1978) to represent the emotions.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tripathy et al. 2020
ICface model is interpretable in that facial attribute vector can be manually defined.
“The model is also interpretable in that the facial attribute vector can be manually defined.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tripathy et al. 2020
ICface eliminates expression of input face by mapping input image to a neutral state representing zero AU values.
“To swap the expression to the target face, ICface eliminates the expression of the input face first, which is done by mapping the input image to a neutral state representing zero AU values.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tripathy et al. 2020
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“To solve these problems, ICFA (Tripathy et al., 2020) proposes to use action units (AUs) (Friesen and Ekman, 1978) to represent the emotions.”
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- Authors
- Tripathy, S., Kannala, J., & Rahtu, E.
- Affiliations
- Tampere University (FI)
- Aalto University (FI)
- Venue
- 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 3374-3383
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2929639767
- Semantic Scholar
- 14927abbd16abbf1c1dbed1a2f70dd0a9bea8120
- Cited by
- 88
- References
- 45
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 14 · 2023: 16 · 2022: 23 · 2021: 10 · 2020: 9 · 2019: 2
- FWCI
- 4.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Tampere University Institutional Repository (Tampere University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Keywords
- face reenactment, facial animation, head pose estimation, facial action units, facial expression control, self-supervised learning, two-stage neural network, video editing, image editing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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