# Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos

Anonymous Author(s)
Submission ID: papers_17189

## What do we do?

## BTimer: Given a monocular video as input, our method reconstructs a 3D Gaussian Splatting (3DGS) representation at any desired timestamp in a feed-forward fashion.

## Technical Contributions

We present the first feed-forward reconstruction model for dynamic scenes using a bullet-time formulation;

BTimer reconstructs a bullet-time scene within 150ms while reaching state-of-the-art performance on both static and dynamic scene datasets, even compared with optimization-based approaches.

## Method Framework

## The model takes as input a sequence of context frames and their Plücker embeddings, along with the context timestamp and target (`bullet') timestamp embeddings. It then directly predicts the 3DGS representation at the bullet timestamp.

## Results

### Qualitative Results

Your browser does not support the video tag.
Qualitative Results on SORA scenes. We pause the video at a frame and move the camera to make a bullet-time effect.
Your browser does not support the video tag.
Qualitative Results on DAVIS dataset. Left is the input video, right is the video rendered from a novel camera trajectory.
Qualitative Results on DyCheck iPhone dataset. Left is the input video, right two are the videos rendered from novel camera trajectories.

### Dynamic Novel View Synthesis Benchmark Results

Baseline results on NVIDIA Dynamic Scene Dataset.
Baseline comparisons on DyCheck iPhone Scenes. We mask areas that are not co-visible.

### Results on Static Scenes

Results on the Tanks & Temples static scenes benchmark.

### Effect of the NTE Module

### Visualization of learned deformation

We visualize the deformation leanred by our model. With only 4 images as input, our model can reconstruct smooth 3D motions.

### Visualization of learned scene flow

We visualize the scene flow learned by our model. We color the Gaussians by the pixel positions they associate with.