RealMaster: Lifting Rendered Scenes into Photorealistic Video
Dana Cohen-Bar, Ido Sobol, Raphael Bensadoun, Shelly Sheynin, Oran Gafni, Or Patashnik, Daniel Cohen-Or, Amit Zohar

TL;DR
RealMaster is a novel method that enhances rendered 3D scene videos into photorealistic videos by leveraging diffusion models, ensuring geometric consistency and high visual quality without requiring anchor frames.
Contribution
The paper introduces a pipeline combining geometric conditioning and diffusion models to lift rendered videos into photorealistic videos, with a trained IC-LoRA model that generalizes beyond the initial pipeline constraints.
Findings
Outperforms existing video editing baselines on GTA-V sequences
Maintains geometry, dynamics, and identity during enhancement
Enables photorealistic video generation without anchor frames
Abstract
State-of-the-art video generation models produce remarkable photorealism, but they lack the precise control required to align generated content with specific scene requirements. Furthermore, without an underlying explicit geometry, these models cannot guarantee 3D consistency. Conversely, 3D engines offer granular control over every scene element and provide native 3D consistency by design, yet their output often remains trapped in the "uncanny valley". Bridging this sim-to-real gap requires both structural precision, where the output must exactly preserve the geometry and dynamics of the input, and global semantic transformation, where materials, lighting, and textures must be holistically transformed to achieve photorealism. We present RealMaster, a method that leverages video diffusion models to lift rendered video into photorealistic video while maintaining full alignment with the…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis · Human Motion and Animation
