Search Results for author:
Found 5 papers, 2 papers with code
Rotationally-Temporally Consistent Novel View Synthesis of Human Performance Video
Second, we introduce a novel loss to explicitly enforce consistency across generated views both in space and in time.
Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling
We present a method that enables synthesizing novel views and novel poses of arbitrary human performers from sparse multi-view images.
Learning Dynamic View Synthesis With Few RGBD Cameras
Experiments on the HTI dataset show that our method outperforms the baseline per-frame image fidelity and spatial-temporal consistency.
Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering
To tackle this, we propose Neural Human Performer, a novel approach that learns generalizable neural radiance fields based on a parametric human body model for robust performance capture.
Ranked #3 on
Generalizable Novel View Synthesis
on ZJU-MoCap
