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Found 8 papers, 1 papers with code
HUMOS: Human Motion Model Conditioned on Body Shape
Generating realistic human motion is essential for many computer vision and graphics applications.
EPOCH: Jointly Estimating the 3D Pose of Cameras and Humans
These inputs are obtained from RegNet, which starts from a single image and provides estimates for the 2D pose and camera parameters.
fNeRF: High Quality Radiance Fields from Practical Cameras
In recent years, the development of Neural Radiance Fields has enabled a previously unseen level of photo-realistic 3D reconstruction of scenes and objects from multi-view camera data.
Personalized 3D Human Pose and Shape Refinement
To address this issue, we propose to construct dense correspondences between initial human model estimates and the corresponding images that can be used to refine the initial predictions.
Speech Driven Tongue Animation
In this work, we introduce a large-scale speech and mocap dataset that focuses on capturing tongue, jaw, and lip motion.
ANR: Articulated Neural Rendering for Virtual Avatars
The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the resulting images.
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
At the level of patches, objects across different categories share similarities, which leads to more generalizable models.
TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video
We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video.
