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Found 13 papers, 5 papers with code
InteractAvatar: Modeling Hand-Face Interaction in Photorealistic Avatars with Deformable Gaussians
With the rising interest from the community in digital avatars coupled with the importance of expressions and gestures in communication, modeling natural avatar behavior remains an important challenge across many industries such as teleconferencing, gaming, and AR/VR.
UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping
The compressed UVGS can be treated as typical RGB images.
Multi-view Image Diffusion via Coordinate Noise and Fourier Attention
Recently, text-to-image generation with diffusion models has made significant advancements in both higher fidelity and generalization capabilities compared to previous baselines.
Generalizable Human Gaussians for Sparse View Synthesis
To tackle this challenge, this paper leverages recent advancements in Gaussian Splatting and introduces a new method to learn generalizable human Gaussians that allows photorealistic and accurate view-rendering of a new human subject from a limited set of sparse views in a feed-forward manner.
Towards Realistic Generative 3D Face Models
By combining 2D face generative models with semantic face manipulation, this method enables editing of detailed 3D rendered faces.
Ranked #6 on
3D Face Reconstruction
on REALY (side-view)
Unpaired Image Translation via Vector Symbolic Architectures
Image-to-image translation has played an important role in enabling synthetic data for computer vision.
Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation.
Self-Supervised Object Detection via Generative Image Synthesis
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection.
Self-Supervised Real-to-Sim Scene Generation
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning
However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap.
Meta-Sim: Learning to Generate Synthetic Datasets
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get.
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone.
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
We present a system for training deep neural networks for object detection using synthetic images.
