# Zero-shot Synthetic Video Realism Enhancement via Structure-aware Denoising

We propose an approach to enhancing synthetic video realism, which can re-render synthetic videos from a simulator in photorealisic fashion. Our realism enhancement approach is a zero-shot framework that focuses on preserving the multi-level structures from synthetic videos into the enhanced one in both spatial and temporal domains, built upon a diffusion video foundational model without further fine-tuning. Specifically, we incorporate an effective modification to have the generation/denoising process conditioned on estimated structure-aware information from the synthetic video, such as depth maps, semantic maps, and edge maps, by another model, rather than extracting the information from a simulator. This guidance ensures that the enhanced videos are consistent with the original synthetic video at both structural and semantic levels. Our approach is a simple yet general and powerful approach to enhancing synthetic video realism: we show that our approach outperforms alternative baselines in structural consistency with the original video while maintaining state-of-the-art photorealism quality in our experiments.

## Overview

## Qualitative Results

CARLA

Ours

CARLA

Ours

CARLA

WAN2.1 VACE

Cosmos-Transfer

Ours

CARLA

WAN2.1 VACE

Cosmos-Transfer

Ours

CARLA

WAN2.1 VACE

Cosmos-Transfer

Ours

CARLA

WAN2.1 VACE

Cosmos-Transfer

Ours

CARLA

Cosmos-Transfer

Ours

### More Examples

CARLA

WAN2.1 VACE

Cosmos-Transfer

Ours

### Qualitative Results on GTA V

GTA

Ours

GTA

Ours

GTA

Ours

### Compared to FLUX

CARLA

FLUX controlnet frame by frame

Ours

### Indoor Environment generated by Behavior Vision Suite

Simulator

Ours