3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow

• URL: http://arxiv.org/abs/2404.09819v1
• Date: Mon, 15 Apr 2024 14:20:07 GMT
• Title: 3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow
• Authors: Felix Taubner, Prashant Raina, Mathieu Tuli, Eu Wern Teh, Chul Lee, Jinmiao Huang,
• Abstract summary: We propose a novel face tracker, FlowFace, that introduces an innovative 2D alignment network for dense per-vertex alignment. Unlike prior work, FlowFace is trained on high-quality 3D scan annotations rather than weak supervision or synthetic data. Our method exhibits superior performance on both custom and publicly available benchmarks.
• Score: 15.479024531161476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When working with 3D facial data, improving fidelity and avoiding the uncanny valley effect is critically dependent on accurate 3D facial performance capture. Because such methods are expensive and due to the widespread availability of 2D videos, recent methods have focused on how to perform monocular 3D face tracking. However, these methods often fall short in capturing precise facial movements due to limitations in their network architecture, training, and evaluation processes. Addressing these challenges, we propose a novel face tracker, FlowFace, that introduces an innovative 2D alignment network for dense per-vertex alignment. Unlike prior work, FlowFace is trained on high-quality 3D scan annotations rather than weak supervision or synthetic data. Our 3D model fitting module jointly fits a 3D face model from one or many observations, integrating existing neutral shape priors for enhanced identity and expression disentanglement and per-vertex deformations for detailed facial feature reconstruction. Additionally, we propose a novel metric and benchmark for assessing tracking accuracy. Our method exhibits superior performance on both custom and publicly available benchmarks. We further validate the effectiveness of our tracker by generating high-quality 3D data from 2D videos, which leads to performance gains on downstream tasks.

Related papers

• EmbodiedSAM: Online Segment Any 3D Thing in Real Time [61.2321497708998]
  Embodied tasks require the agent to fully understand 3D scenes simultaneously with its exploration. An online, real-time, fine-grained and highly-generalized 3D perception model is desperately needed.
  arXiv  Detail & Related papers  (2024-08-21T17:57:06Z)
• TAPVid-3D: A Benchmark for Tracking Any Point in 3D [63.060421798990845]
  We introduce a new benchmark, TAPVid-3D, for evaluating the task of Tracking Any Point in 3D. This benchmark will serve as a guidepost to improve our ability to understand precise 3D motion and surface deformation from monocular video.
  arXiv  Detail & Related papers  (2024-07-08T13:28:47Z)
• Compressed Deepfake Video Detection Based on 3D Spatiotemporal Trajectories [10.913345858983275]
  Deepfake technology by malicious actors poses a potential threat to nations, societies, and individuals. In this paper, we propose a deepfake video detection method based on 3Dtemporal motion features. Our method yields satisfactory results and showcases its potential for practical applications.
  arXiv  Detail & Related papers  (2024-04-28T11:48:13Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior [57.986512832738704]
  We present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model.
  arXiv  Detail & Related papers  (2024-03-14T07:39:59Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• Fake It Without Making It: Conditioned Face Generation for Accurate 3D Face Reconstruction [5.079602839359523]
  We present a method to generate a large-scale synthesised dataset of 250K photorealistic images and their corresponding shape parameters and depth maps, which we call SynthFace. Our synthesis method conditions Stable Diffusion on depth maps sampled from the FLAME 3D Morphable Model (3DMM) of the human face, allowing us to generate a diverse set of shape-consistent facial images that is designed to be balanced in race and gender. We propose ControlFace, a deep neural network, trained on SynthFace, which achieves competitive performance on the NoW benchmark, without requiring 3D supervision or manual 3D asset creation.
  arXiv  Detail & Related papers  (2023-07-25T16:42:06Z)
• DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation [18.511092587156657]
  We present a novel self-supervised method for learning dense 3D facial geometry from face videos. We also propose a strategy to learn pixel-level uncertainties to perceive more reliable rigid-motion pixels for geometry learning. We develop a 3D-aware cross-modal (ie, appearance and depth) attention mechanism to capture facial geometries in a coarse-to-fine manner.
  arXiv  Detail & Related papers  (2023-05-10T14:58:33Z)
• RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs [13.11105614044699]
  We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR) A large-scale pseudo 2D&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks.
  arXiv  Detail & Related papers  (2023-02-10T19:40:26Z)
• DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation [56.56575063461169]
  DeepFaceFlow is a robust, fast, and highly-accurate framework for the estimation of 3D non-rigid facial flow. Our framework was trained and tested on two very large-scale facial video datasets. Given registered pairs of images, our framework generates 3D flow maps at 60 fps.
  arXiv  Detail & Related papers  (2020-05-14T23:56:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.