Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters
Not on the map. Why?
Abstract (arXiv)
Animating virtual characters has always been a fundamental research problem in virtual reality (VR). Facial animations play a crucial role as they effectively convey emotions and attitudes of virtual humans. However, creating such facial animations can be challenging, as current methods often involve utilization of expensive motion capture devices or significant investments of time and effort from human animators in tuning animation parameters. In this paper, we propose a holistic solution to automatically animate virtual human faces. In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients. This method offers the flexibility of generating animations with characters of different appearances and blendshape topologies. Second, a practical toolkit was developed using Unity 3D, making it compatible with the most popular VR applications. The toolkit accepts both image and video as input to animate the target virtual human faces and enables users to manipulate the animation results. Furthermore, inspired by the spirit of Human-in-the-loop (HITL), we leveraged user feedback to further improve the performance of the model and toolkit, thereby increasing the customization properties to suit user preferences. The whole solution, for which we will make the code public, has the potential to accelerate the generation of facial animations for use in VR applications.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 21 Feb 2024.
Builds on
8 of 38 references.
References · 8
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2019Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Setcited by 482
- 2018Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Networkcited by 195
- 2017End-to-end 3D face reconstruction with deep neural networkscited by 47
- 2020Personalized Face Modeling for Improved Face Reconstruction and Motion Retargetingcited by 27
- 2020Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creationcited by 19
- 2019Face-to-Parameter Translation for Game Character Auto-Creationcited by 14
- 2017Unsupervised Creation of Parameterized Avatarscited by 6
Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 2.
6 papers.
- 2024MixedGaussianAvatar: Realistically and Geometrically Accurate Head Avatar via Mixed 2D-3D Gaussianscited by 4
- 2025DiffusionTalker: Efficient and Compact Speech-Driven 3D Talking Head via Personalizer-Guided Distillationcited by 4
- 2026RSATalker: Realistic Socially-Aware Talking Head Generation for Multi-Turn Conversationcited by 2
- 2025FactorPortrait: Controllable Portrait Animation via Disentangled Expression, Pose, and Viewpointcited by 1
- 2025Face-MakeUpV2: Facial Consistency Learning for Controllable Text-to-Image Generationcited by 0
- 2026Conversational Human Audio-visual Talking Dialogue Generationcited by 0
- 2026Conversational Human Audio-visual Talking Dialogue Generation2 Jul 2026
- 2026RSATalker: Realistic Socially-Aware Talking Head Generation for Multi-Turn Conversation15 Jan 2026
- 2025FactorPortrait: Controllable Portrait Animation via Disentangled Expression, Pose, and Viewpoint12 Dec 2025
- 2025Face-MakeUpV2: Facial Consistency Learning for Controllable Text-to-Image Generation17 Oct 2025
- 2025DiffusionTalker: Efficient and Compact Speech-Driven 3D Talking Head via Personalizer-Guided Distillation23 Mar 2025
- 2024MixedGaussianAvatar: Realistically and Geometrically Accurate Head Avatar via Mixed 2D-3D Gaussians6 Dec 2024