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Found 28 papers, 9 papers with code
RobustFusion: Human Volumetric Capture with Data-driven Visual Cues using a RGBD Camera
To enable robust tracking, we embrace both the initial model and the various visual cues into a novel performance capture scheme with hybrid motion optimization and semantic volumetric fusion, which can successfully capture challenging human motions under the monocular setting without pre-scanned detailed template and owns the reinitialization ability to recover from tracking failures and the disappear-reoccur scenarios.
Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians
Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups.
Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling
Overall, our method can create lifelike avatars with dynamic, realistic and generalized appearances.
Leveraging Intrinsic Properties for Non-Rigid Garment Alignment
To achieve wrinkle-level as well as texture-level alignment, we present a novel coarse-to-fine two-stage method that leverages intrinsic manifold properties with two neural deformation fields, in the 3D space and the intrinsic space, respectively.
Control4D: Efficient 4D Portrait Editing with Text
We introduce Control4D, an innovative framework for editing dynamic 4D portraits using text instructions.
AvatarReX: Real-time Expressive Full-body Avatars
We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data.
PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modeling
To this end, we present PoseVocab, a novel pose encoding method that encourages the network to discover the optimal pose embeddings for learning the dynamic human appearance.
CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition
In this way, the clothing deformations are disentangled such that the pose-dependent wrinkles can be better learned and applied to unseen poses.
Tensor4D: Efficient Neural 4D Decomposition for High-Fidelity Dynamic Reconstruction and Rendering
The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor.
Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering
The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor.
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras
At its core is a novel diffusion-based stereo module, which introduces diffusion models, a type of powerful generative models, into the iterative stereo matching network.
Learning Implicit Templates for Point-Based Clothed Human Modeling
We present FITE, a First-Implicit-Then-Explicit framework for modeling human avatars in clothing.
AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture
Then given a monocular RGB video of this subject, our method integrates information from both the image observation and the avatar prior, and accordingly recon-structs high-fidelity 3D textured models with dynamic details regardless of the visibility.
ProbNVS: Fast Novel View Synthesis with Learned Probability-Guided Sampling
Existing state-of-the-art novel view synthesis methods rely on either fairly accurate 3D geometry estimation or sampling of the entire space for neural volumetric rendering, which limit the overall efficiency.
Structured Local Radiance Fields for Human Avatar Modeling
These local radiance fields not only leverage the flexibility of implicit representation in shape and appearance modeling, but also factorize cloth deformations into skeleton motions, node residual translations and the dynamic detail variations inside each individual radiance field.
High-Fidelity Human Avatars From a Single RGB Camera
To cope with the complexity of textures and generate photo-realistic results, we propose a reference-based neural rendering network and exploit a bottom-up sharpening-guided fine-tuning strategy to obtain detailed textures.
HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars
To handle complicated motions (e. g., self-occlusions), we then leverage the encoded information on the UV manifold to construct a 3D volumetric representation based on a dynamic pose-conditioned neural radiance field.
Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors
Human volumetric capture is a long-standing topic in computer vision and computer graphics.
DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras
We propose DeepMultiCap, a novel method for multi-person performance capture using sparse multi-view cameras.
POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture
By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions.
Deep Implicit Templates for 3D Shape Representation
Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power.
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments.
PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction
To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function.
Ranked #2 on
3D Human Reconstruction
on CAPE
Robust 3D Self-portraits in Seconds
In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera.
DeepHuman: 3D Human Reconstruction from a Single Image
We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D human reconstruction from a single RGB image.
SimulCap : Single-View Human Performance Capture with Cloth Simulation
This paper proposes a new method for live free-viewpoint human performance capture with dynamic details (e. g., cloth wrinkles) using a single RGBD camera.
HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs
We propose a light-weight and highly robust real-time human performance capture method based on a single depth camera and sparse inertial measurement units (IMUs).
DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor
We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance.
