mlsanityData to 14 Oct 2026

Geometry-Driven Shadow Harmonisation for Composited Faces: A Multiplicative, Albedo-Preserving Relighting Pipeline

arXiv 2609.17740 ↗First posted 15 Sep 2026cs.CVcited by 1

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Abstract (arXiv)

Face swapping and face compositing pipelines routinely produce a face that is geometrically well aligned but photometrically implausible: the donor face carries flat, near-frontal studio illumination while the host body and background carry directional scene light. Most existing remedies re-synthesise the face through colour transfer, neural relighting, or inverse rendering, and therefore risk altering identity, skin tone, and texture. We present a conservative alternative: geometry-driven form-shadow injection. The pipeline never repaints the face. It estimates a per-pixel gain field g∈[gmin,1] from a rasterised 3D face proxy and multiplies it channel-uniformly onto linear RGB, so the operator can only darken and cannot shift chromaticity. A dense landmark mesh is rasterised into a depth buffer, from which we derive surface normals, a cavity term, and screen-space cast shadows. Key-light direction is estimated from host-side cues (body, background, hair halo); on-face cues are downweighted because they recover the donor's lighting. Shadow magnitude is not matched to the host: it is set by a three-parameter transfer (τ,σ,gmin). The shading field is divided by its 75th percentile over skin, then gated, scaled, clamped, smoothed, and re-clipped inside a feathered, skin-gated face mask. On an analytic face heightfield, the default (τ,σ,gmin)=(0.90,0.45,0.82) modifies 56.5% of face pixels with mean gain 0.938 (0.890 on modified pixels) and drives 3.4% of pixels to the floor. Hue invariance is a corollary of the operator. We analyse the transfer in closed form, ablate its parameters, and discuss failure modes of a monotone, darkening-only formulation, including double-shadowing of non-flat donors.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 15 Sep 2026.

Builds on

5 of 13 references.

References · 5

  1. 2019Single Image Portrait Relightingcited by 152
  2. 2019DoveNet: Deep Image Harmonization via Domain Verificationcited by 135
  3. 2017Deep Image Harmonizationcited by 120
  4. 2017SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wildcited by 110
  5. 2019Real-time Facial Surface Geometry from Monocular Video on Mobile GPUscited by 73

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.