D-Rex : Diffusion Rendering for Relightable Expressive Avatars

We present D-Rex, a person-specific framework for photore-alistic, relightable, expressive, and animatable full-body human avatarswith free-viewpoint rendering. Existing methods for relightable full-bodyavatars rely on explicit 3D intrinsic decomposition with analytic re-flectance models, which require accurate geometry registration and care-ful optimization to capture realistic light transport effects. This tightcoupling of relighting with avatar modeling has hindered expressiveness:to our knowledge, no existing method demonstrates strong facial anima-tion alongside relighting, limiting applicability in telepresence, gaming,and virtual production. We propose to decouple relighting entirely fromavatar modeling by treating it as an image-space post-process: a learnedtranslation from flat-lit, albedo-like renderings to a target HDR illumina-tion. To this end, we leverage the strong generative prior of a pre-trainedvideo diffusion relighting model, fine-tuned via LoRA on paired flat-litand relit frames captured in a light stage. The flat-lit driving framesare produced by an independent expressive full-body avatar frameworktrained under white-light conditions, requiring no modification to sup-port relighting, making D-Rex directly applicable to any white-lightavatar system. We demonstrate that D-Rex enables view- and tempo-rally consistent relighting while faithfully preserving expressive motionand fine-grained facial detail, outperforming physically-based relightableavatar baselines. Project page: https://vcai.mpi-inf.mpg.de/projects/DRex/