Search Results for author:
Found 10 papers, 3 papers with code
GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians
Hairstyle reflects culture and ethnicity at first glance.
MVHuman: Tailoring 2D Diffusion with Multi-view Sampling For Realistic 3D Human Generation
Recent months have witnessed rapid progress in 3D generation based on diffusion models.
Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream
We also provide an on-the-fly reconstruction scheme of the dynamic/static radiance fields via efficient motion-prior searching.
NEPHELE: A Neural Platform for Highly Realistic Cloud Radiance Rendering
Using such a cloud platform compatible with neural rendering, we further showcase the capabilities of our cloud radiance rendering through a series of applications, ranging from cloud VR/AR rendering.
NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions
We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HODome, that consists of $\sim$75M frames on 10 subjects interacting with 23 objects.
HumanGen: Generating Human Radiance Fields with Explicit Priors
With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation.
Artemis: Articulated Neural Pets with Appearance and Motion synthesis
Our ARTEMIS enables interactive motion control, real-time animation, and photo-realistic rendering of furry animals.
Few-shot Neural Human Performance Rendering from Sparse RGBD Videos
To fill this gap, in this paper we propose a few-shot neural human rendering approach (FNHR) from only sparse RGBD inputs, which exploits the temporal and spatial redundancy to generate photo-realistic free-view output of human activities.
Convolutional Neural Opacity Radiance Fields
In this paper, we propose a novel scheme to generate opacity radiance fields with a convolutional neural renderer for fuzzy objects, which is the first to combine both explicit opacity supervision and convolutional mechanism into the neural radiance field framework so as to enable high-quality appearance and global consistent alpha mattes generation in arbitrary novel views.
GNeRF: GAN-based Neural Radiance Field without Posed Camera
We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and even randomly initialized camera poses.
