  Generating ``bullet-time'' effects of human free-viewpoint videos is critical
for immersive visual effects and VR/AR experience. Recent neural advances still
lack the controllable and interactive bullet-time design ability for human
free-viewpoint rendering, especially under the real-time, dynamic and general
setting for our trajectory-aware task. To fill this gap, in this paper we
propose a neural interactive bullet-time generator (iButter) for
photo-realistic human free-viewpoint rendering from dense RGB streams, which
enables flexible and interactive design for human bullet-time visual effects.
Our iButter approach consists of a real-time preview and design stage as well
as a trajectory-aware refinement stage. During preview, we propose an
interactive bullet-time design approach by extending the NeRF rendering to a
real-time and dynamic setting and getting rid of the tedious per-scene
training. To this end, our bullet-time design stage utilizes a hybrid training
set, light-weight network design and an efficient silhouette-based sampling
strategy. During refinement, we introduce an efficient trajectory-aware scheme
within 20 minutes, which jointly encodes the spatial, temporal consistency and
semantic cues along the designed trajectory, achieving photo-realistic
bullet-time viewing experience of human activities. Extensive experiments
demonstrate the effectiveness of our approach for convenient interactive
bullet-time design and photo-realistic human free-viewpoint video generation.

Computer Vision and Pattern Recognition;Graphics