most citedLaughTalk: Expressive 3D Talking Head Generation with Laughter

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cs.CV2024

Enhancing Speech-Driven 3D Facial Animation with Audio-Visual Guidance from Lip Reading Expert

Han EunGi, Oh Hyun-Bin, Kim Sung-Bin +4

Speech-driven 3D facial animation has recently garnered attention due to its cost-effective usability in multimedia production. However, most current advances overlook the intellig…

cs.CV2024

MultiTalk: Enhancing 3D Talking Head Generation Across Languages with Multilingual Video Dataset

Kim Sung-Bin, Lee Chae-Yeon, Gihun Son +4

Recent studies in speech-driven 3D talking head generation have achieved convincing results in verbal articulations. However, generating accurate lip-syncs degrades when applied to…

cs.CV20242 cited

Revisiting Learning-based Video Motion Magnification for Real-time Processing

Hyunwoo Ha, Oh Hyun-Bin, Kim Jun-Seong +6

Video motion magnification is a technique to capture and amplify subtle motion in a video that is invisible to the naked eye. The deep learning-based prior work successfully demons…

cs.CV20231 cited

LaughTalk: Expressive 3D Talking Head Generation with Laughter

Kim Sung-Bin, Lee Hyun, Da Hye Hong +3

Laughter is a unique expression, essential to affirmative social interactions of humans. Although current 3D talking head generation methods produce convincing verbal articulations…

cs.CV20231 cited

A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization

Kim Youwang, Lee Hyun, Kim Sung-Bin +3

We propose NeuFace, a 3D face mesh pseudo annotation method on videos via neural re-parameterized optimization. Despite the huge progress in 3D face reconstruction methods, generat…

cs.CV2023

The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation

Kwon Byung-Ki, Kim Sung-Bin, Tae-Hyun Oh

Recent work on dense optical flow has shown significant progress, primarily in a supervised learning manner requiring a large amount of labeled data. Due to the expensiveness of ob…