TranSplat: Instant Cross-Scene Object Relighting in Gaussian Splatting via Spherical Harmonic Transfer

Boyang (Tony) Yu*1, Yanlin Jin*1, Yun He2, Akshat Dave3, Guha Balakrishnan1,
*Equal Contribution, 1Rice University, 2University of Maryland 3Stony Brook University
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⚠️ This site is under active development. Displayed results may not be up-to-date (e.g., shadows may be missing). More advanced 3D demonstrations are coming soon.

Transplat relighting on synthetic TensoIR dataset

Abstract

We present TranSplat, a method for fast and accurate object relighting for the 3D Gaussian Splatting (GS) framework when transferring a 3D object from a source GS scene to a target GS scene. TranSplat is based on a theoretical radiance transfer identity for cross-scene relighting of objects with radially symmetric BRDFs that involves only taking simple products of spherical harmonic appearance coefficients of the object, source, and target environment maps without any explicit computation of scene quantities (e.g., the BRDFs themselves). TranSplat is the first method to demonstrate how this theoretical identity may be used to perform relighting within the GS framework, and furthermore, by automatically inferring unknown source and target environment maps directly from the source and target scene GS representations.

We evaluated TranSplat on several synthetic and real-world scenes and objects, demonstrating comparable 3D object relighting performance to recent conventional inverse rendering-based GS methods with a fraction of their runtime. While TranSplat is theoretically best-suited for radially symmetric BRDFs, results demonstrate that TranSplat still offers perceptually realistic renderings on real scenes and opens a valuable, lightweight path forward to relighting with the GS framework.

Motivation

The Gaussian Splatting (GS) framework represents a scene as optimizable Gaussian primitives, offering fast, differentiable rendering and semantic decomposition. Building on GS, recent methods enable interactive scene editing—recoloring objects or inserting new ones—by manipulating these primitives.

We tackle the fundamental task of transferring a 3D object from one scene to another with realistic relighting. This involves (1) extracting and aligning the object in 3D, and (2) removing source lighting effects and applying target-scene illumination without estimating explicit material properties. To meet these challenges, we introduce TranSplat.

Method

TranSplat Method Overview
Figure 1: Overview of the TranSplat pipeline.

Relighting Results

Novel views of the relighting results on TensoIR dataset across different environment maps. Click to select the target environment map.

Fireplace Env Map

Fireplace

Sunset Env Map

Sunset

Forest Env Map

Forest

Source Env Map: City

Source Env Map: City

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Target Env Map

Target Env Map

Experiments

TranSplat Segmentation Results
Figure 2: Example of TranSplat extracting fine details of an object when fitting a GS model to a source scene.
TranSplat Relighting Results
Figure 2: Qualitative comparison of TranSplat with recent Gaussian relighting methods.

BibTeX

@misc{yu2025transplatinstantcrosssceneobject,
      title={TranSplat: Instant Cross-Scene Object Relighting in Gaussian Splatting via Spherical Harmonic Transfer}, 
      author={Boyang Yu and Yanlin Jin and Yun He and Akshat Dave and Guha Balakrishnan},
      year={2025},
      eprint={2503.22676},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.22676}, 
}