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Found 21 papers, 5 papers with code
Relightable Full-Body Gaussian Codec Avatars
This allows us to learn diffuse radiance transfer in a local coordinate frame, which disentangles the local radiance transfer from the articulation of the body.
Learning to Stabilize Faces
Nowadays, it is possible to scan faces and automatically register them with high quality.
FabricDiffusion: High-Fidelity Texture Transfer for 3D Garments Generation from In-The-Wild Clothing Images
We introduce FabricDiffusion, a method for transferring fabric textures from a single clothing image to 3D garments of arbitrary shapes.
Authentic Hand Avatar from a Phone Scan via Universal Hand Model
In this paper, we present a universal hand model (UHM), which 1) can universally represent high-fidelity 3D hand meshes of arbitrary identities (IDs) and 2) can be adapted to each person with a short phone scan for the authentic hand avatar.
Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration
Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data.
Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input
Clothing is an important part of human appearance but challenging to model in photorealistic avatars.
Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images
Furthermore, we introduce a novel neural rendering framework based on rasterization of the learned hair strands.
Multiface: A Dataset for Neural Face Rendering
Along with the release of the dataset, we conduct ablation studies on the influence of different model architectures toward the model's interpolation capacity of novel viewpoint and expressions.
Drivable Volumetric Avatars using Texel-Aligned Features
To circumvent this, we propose a novel volumetric avatar representation by extending mixtures of volumetric primitives to articulated objects.
Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing
The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry.
Garment Avatars: Realistic Cloth Driving using Pattern Registration
Here, we propose an end-to-end pipeline for building drivable representations for clothing.
Modeling Clothing as a Separate Layer for an Animatable Human Avatar
To address these difficulties, we propose a method to build an animatable clothed body avatar with an explicit representation of the clothing on the upper body from multi-view captured videos.
Driving-Signal Aware Full-Body Avatars
The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation.
MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video
A differentiable renderer is utilized to align our captured shapes to the input frames by minimizing the difference in both silhouette, segmentation, and texture.
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Learning latent representations of registered meshes is useful for many 3D tasks.
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
Extensive experiments in several typical lifelong learning scenarios demonstrate that our method outperforms the state-of-the-art methods in both accuracies on new tasks and performance preservation on old tasks.
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices.
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required.
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs
Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest Iphone X.
Modeling Facial Geometry Using Compositional VAEs
We propose a method for learning non-linear face geometry representations using deep generative models.
Learning Patch Reconstructability for Accelerating Multi-View Stereo
By prioritizing stereo matching on a subset of patches that are highly reconstructable and also cover the 3D surface, we are able to accelerate MVS with minimal reduction in accuracy and completeness.
