FACTS: Facial Animation Creation using the Transfer of Styles
Not on the map. Why?
Abstract (arXiv)
The ability to accurately capture and express emotions is a critical aspect of creating believable characters in video games and other forms of entertainment. Traditionally, this animation has been achieved with artistic effort or performance capture, both requiring costs in time and labor. More recently, audio-driven models have seen success, however, these often lack expressiveness in areas not correlated to the audio signal. In this paper, we present a novel approach to facial animation by taking existing animations and allowing for the modification of style characteristics. Specifically, we explore the use of a StarGAN to enable the conversion of 3D facial animations into different emotions and person-specific styles. We are able to maintain the lip-sync of the animations with this method thanks to the use of a novel viseme-preserving loss.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 44 references that are in the map. First posted on arXiv 18 Jul 2023.
Builds on
24 of 44 references.
References · 24
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2017Improved Training of Wasserstein GANscited by 2,223
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
- 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
- 2020A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wildcited by 669
- 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
- 20193D Morphable Face Models -- Past, Present and Futurecited by 269
12 more
- 2019Capture, Learning, and Synthesis of 3D Speaking Stylescited by 257
- 2019Neural Voice Puppetry: Audio-driven Facial Reenactmentcited by 249
- 2021FaceFormer: Speech-Driven 3D Facial Animation with Transformerscited by 208
- 2023Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generationcited by 142
- 2023Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expertcited by 98
- 2020Towards Automatic Face-to-Face Translationcited by 95
- 2022Imitator: Personalized Speech-driven 3D Facial Animationcited by 73
- 2021LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces from Video using Pose and Lighting Normalizationcited by 67
- 2019Neural Style-Preserving Visual Dubbingcited by 47
- 2022Memories are One-to-Many Mapping Alleviators in Talking Face Generationcited by 22
- 2022Talking Face Generation with Multilingual TTScited by 18
- 20213D-TalkEmo: Learning to Synthesize 3D Emotional Talking Headcited by 10
Built on by
Not cited yet.