ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events

Abstract

Recent advances in neural rendering have enabled the 3D reconstruction of dynamic humans from monocular videos, with applications in robotics. However, it is still challenging to reconstruct clear humans from in-the-wild video encountering motion blur, causing shape and appearance inconsistencies, especially in blurry regions like hands and legs. In this paper, we propose ExFMan, the first neural rendering framework that unveils the possibility of rendering high-quality humans in rapid motion with a hybrid frame-based RGB and bio-inspired event camera. The ``out-of-the-box'' insight is to leverage the high temporal information of event data in a complementary manner and adaptively reweight the effect of losses for both RGB frames and events in the local regions, according to the velocity of the rendered human. This significantly mitigates the inconsistency associated with motion blur in the RGB frames. Specifically, we first formulate a velocity field of the 3D body in the canonical space and render it to image space to identify the body parts with motion blur. We then propose two novel losses, i.e., velocity-aware photometric loss and velocity-relative event loss, to optimize the neural human for both modalities under the guidance of the estimated velocity. In addition, we incorporate novel pose regularization and alpha losses to facilitate continuous pose and clear boundary. Extensive experiments on synthetic and real-world datasets demonstrate that ExFMan can reconstruct sharper and higher quality humans over the compared baselines and the state-of-the-art methods for diverse blurry subjects.