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Found 10 papers, 0 papers with code
InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion
Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object.
A Local Appearance Model for Volumetric Capture of Diverse Hairstyle
In this paper, we present a novel method for creating high-fidelity avatars with diverse hairstyles.
Relightable Gaussian Codec Avatars
The fidelity of relighting is bounded by both geometry and appearance representations.
Differentiable Display Photometric Stereo
In this paper, we present differentiable display photometric stereo (DDPS), addressing an often overlooked challenge in display photometric stereo: the design of display patterns.
Event Fusion Photometric Stereo Network
We present a novel method to estimate the surface normal of an object in an ambient light environment using RGB and event cameras.
NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation
The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality.
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.
ORA3D: Overlap Region Aware Multi-view 3D Object Detection
Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly, and the networks' understanding of the scene is often limited to that of a monocular detection network.
Ranked #6 on
Robust Camera Only 3D Object Detection
on nuScenes-C
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture
Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. Yet, hair is a critical component for believable avatars.
Strand-Accurate Multi-View Hair Capture
Thus, in the second stage, we feature a novel strand reconstruction method with the mean-shift to convert the noisy point data to a set of strands.
