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MetaCap: Meta-learning Priors from Multi-View Imagery for Sparse-view Human Performance Capture and Rendering
Our key idea is to meta-learn the radiance field weights solely from potentially sparse multi-view videos, which can serve as a prior when fine-tuning them on sparse imagery depicting the human.
Holoported Characters: Real-time Free-viewpoint Rendering of Humans from Sparse RGB Cameras
At inference, our method only requires four camera views of the moving actor and the respective 3D skeletal pose.
NeuralHOFusion: Neural Volumetric Rendering under Human-object Interactions
In this paper, we propose NeuralHOFusion, a neural approach for volumetric human-object capture and rendering using sparse consumer RGBD sensors.
Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactions
In this paper, we propose a neural human performance capture and rendering system to generate both high-quality geometry and photo-realistic texture of both human and objects under challenging interaction scenarios in arbitrary novel views, from only sparse RGB streams.
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